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Remote Sens., Volume 9, Issue 1 (January 2017)

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Description Unmanned Aerial Systems (UAS) or drones feature increasing popularity due to their ability to [...] Read more.
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Editorial

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Open AccessEditorial Acknowledgement to Reviewers of Remote Sensing in 2016
Remote Sens. 2017, 9(1), 62; doi:10.3390/rs9010062
Received: 12 January 2017 / Accepted: 12 January 2017 / Published: 12 January 2017
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Abstract The editors of Remote Sensing would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016.[...] Full article

Research

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Open AccessArticle An Integrated GNSS/INS/LiDAR-SLAM Positioning Method for Highly Accurate Forest Stem Mapping
Remote Sens. 2017, 9(1), 3; doi:10.3390/rs9010003
Received: 2 September 2016 / Revised: 23 November 2016 / Accepted: 21 December 2016 / Published: 23 December 2016
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Abstract
Forest mapping, one of the main components of performing a forest inventory, is an important driving force in the development of laser scanning. Mobile laser scanning (MLS), in which laser scanners are installed on moving platforms, has been studied as a convenient measurement
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Forest mapping, one of the main components of performing a forest inventory, is an important driving force in the development of laser scanning. Mobile laser scanning (MLS), in which laser scanners are installed on moving platforms, has been studied as a convenient measurement method for forest mapping in the past several years. Positioning and attitude accuracies are important for forest mapping using MLS systems. Inertial Navigation Systems (INSs) and Global Navigation Satellite Systems (GNSSs) are typical and popular positioning and attitude sensors used in MLS systems. In forest environments, because of the loss of signal due to occlusion and severe multipath effects, the positioning accuracy of GNSS is severely degraded, and even that of GNSS/INS decreases considerably. Light Detection and Ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM) can achieve higher positioning accuracy in environments containing many features and is commonly implemented in GNSS-denied indoor environments. Forests are different from an indoor environment in that the GNSS signal is available to some extent in a forest. Although the positioning accuracy of GNSS/INS is reduced, estimates of heading angle and velocity can maintain high accurate even with fewer satellites. GNSS/INS and the LiDAR-based SLAM technique can be effectively integrated to form a sustainable, highly accurate positioning and mapping solution for use in forests without additional hardware costs. In this study, information such as heading angles and velocities extracted from a GNSS/INS is utilized to improve the positioning accuracy of the SLAM solution, and two information-aided SLAM methods are proposed. First, a heading angle-aided SLAM (H-aided SLAM) method is proposed that supplies the heading angle from GNSS/INS to SLAM. Field test results show that the horizontal positioning accuracy of an entire trajectory of 800 m is 0.13 m and is significantly improved (by 70%) compared to that of a traditional GNSS/INS; second, a more complex information added SLAM solution that utilizes both heading angle and velocity information simultaneously (HV-aided SLAM) is investigated. Experimental results show that the horizontal positioning accuracy can reach a level of six centimetres with the HV-aided SLAM, which is a significant improvement (by 86%). Thus, a more accurate forest map is obtained by the proposed integrated method. Full article
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
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Open AccessArticle IceMap250—Automatic 250 m Sea Ice Extent Mapping Using MODIS Data
Remote Sens. 2017, 9(1), 70; doi:10.3390/rs9010070
Received: 24 October 2016 / Revised: 4 January 2017 / Accepted: 9 January 2017 / Published: 13 January 2017
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Abstract
The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking
[...] Read more.
The sea ice cover in the North evolves at a rapid rate. To adequately monitor this evolution, tools with high temporal and spatial resolution are needed. This paper presents IceMap250, an automatic sea ice extent mapping algorithm using MODIS reflective/emissive bands. Hybrid cloud-masking using both the MOD35 mask and a visibility mask, combined with downscaling of Bands 3–7 to 250 m, are utilized to delineate sea ice extent using a decision tree approach. IceMap250 was tested on scenes from the freeze-up, stable cover, and melt seasons in the Hudson Bay complex, in Northeastern Canada. IceMap250 first product is a daily composite sea ice presence map at 250 m. Validation based on comparisons with photo-interpreted ground-truth show the ability of the algorithm to achieve high classification accuracy, with kappa values systematically over 90%. IceMap250 second product is a weekly clear sky map that provides a synthesis of 7 days of daily composite maps. This map, produced using a majority filter, makes the sea ice presence map even more accurate by filtering out the effects of isolated classification errors. The synthesis maps show spatial consistency through time when compared to passive microwave and national ice services maps. Full article
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Open AccessArticle Is Spatial Resolution Critical in Urbanization Velocity Analysis? Investigations in the Pearl River Delta
Remote Sens. 2017, 9(1), 80; doi:10.3390/rs9010080
Received: 21 October 2016 / Revised: 2 January 2017 / Accepted: 9 January 2017 / Published: 17 January 2017
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Abstract
Grid-based urbanization velocity analysis of remote sensing imagery is used to measure urban growth rates. However, it remains unclear how critical the spatial resolution of the imagery is to such grid-based approaches. This research therefore investigated how urbanization velocity estimates respond to different
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Grid-based urbanization velocity analysis of remote sensing imagery is used to measure urban growth rates. However, it remains unclear how critical the spatial resolution of the imagery is to such grid-based approaches. This research therefore investigated how urbanization velocity estimates respond to different spatial resolutions, as determined by the grid sizes used. Landsat satellite images of the Pearl River Delta (PRD) in China from the years 2000, 2005, 2010 and 2015 were hierarchically aggregated using different grid sizes. Statistical analyses of urbanization velocity derived using different spatial resolutions (or grid sizes) were used to investigate the relationships between socio-economic indicators and the velocity of urbanization for 27 large cities in PRD. The results revealed that those cities with above-average urbanization velocities remain unaffected by the spatial resolution (or grid-size), and the relationships between urbanization velocities and socio-economic indicators are independent of spatial resolution (or grid sizes) used. Moreover, empirical variogram models, the local variance model, and the geographical variance model all indicated that coarse resolution version (480 m) of Landsat images based on aggregated pixel yielded more appropriate results than the original fine resolution version (30 m), when identifying the characteristics of spatial autocorrelation and spatial structure variability of urbanization patterns and processes. The results conclude that the most appropriate spatial resolution for investigations into urbanization velocities is not always the highest resolution. The resulting patterns of urbanization velocities at different spatial resolutions can be used as a basis for studying the spatial heterogeneity of other datasets with variable spatial resolutions, especially for evaluating the capability of a multi-resolution dataset in reflecting spatial structure and spatial autocorrelation features in an urban environment. Full article
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
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Open AccessArticle Atmospheric Correction Performance of Hyperspectral Airborne Imagery over a Small Eutrophic Lake under Changing Cloud Cover
Remote Sens. 2017, 9(1), 2; doi:10.3390/rs9010002
Received: 24 August 2016 / Revised: 13 December 2016 / Accepted: 19 December 2016 / Published: 23 December 2016
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Abstract
Atmospheric correction of remotely sensed imagery of inland water bodies is essential to interpret water-leaving radiance signals and for the accurate retrieval of water quality variables. Atmospheric correction is particularly challenging over inhomogeneous water bodies surrounded by comparatively bright land surface. We present
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Atmospheric correction of remotely sensed imagery of inland water bodies is essential to interpret water-leaving radiance signals and for the accurate retrieval of water quality variables. Atmospheric correction is particularly challenging over inhomogeneous water bodies surrounded by comparatively bright land surface. We present results of AisaFENIX airborne hyperspectral imagery collected over a small inland water body under changing cloud cover, presenting challenging but common conditions for atmospheric correction. This is the first evaluation of the performance of the FENIX sensor over water bodies. ATCOR4, which is not specifically designed for atmospheric correction over water and does not make any assumptions on water type, was used to obtain atmospherically corrected reflectance values, which were compared to in situ water-leaving reflectance collected at six stations. Three different atmospheric correction strategies in ATCOR4 was tested. The strategy using fully image-derived and spatially varying atmospheric parameters produced a reflectance accuracy of ±0.002, i.e., a difference of less than 15% compared to the in situ reference reflectance. Amplitude and shape of the remotely sensed reflectance spectra were in general accordance with the in situ data. The spectral angle was better than 4.1° for the best cases, in the spectral range of 450–750 nm. The retrieval of chlorophyll-a (Chl-a) concentration using a popular semi-analytical band ratio algorithm for turbid inland waters gave an accuracy of ~16% or 4.4 mg/m3 compared to retrieval of Chl-a from reflectance measured in situ. Using fixed ATCOR4 processing parameters for whole images improved Chl-a retrieval results from ~6 mg/m3 difference to reference to approximately 2 mg/m3. We conclude that the AisaFENIX sensor, in combination with ATCOR4 in image-driven parametrization, can be successfully used for inland water quality observations. This implies that the need for in situ reference measurements is not as strict as has been assumed and a high degree of automation in processing is possible. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Vegetation Dynamics in the Upper Guinean Forest Region of West Africa from 2001 to 2015
Remote Sens. 2017, 9(1), 5; doi:10.3390/rs9010005
Received: 27 October 2016 / Revised: 11 December 2016 / Accepted: 21 December 2016 / Published: 24 December 2016
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Abstract
The Upper Guinea Forest (UGF) region of West Africa is one of the most climatically marginal and human-impacted tropical forest regions in the world. Research on the patterns and drivers of vegetation change is critical for developing strategies to sustain ecosystem services in
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The Upper Guinea Forest (UGF) region of West Africa is one of the most climatically marginal and human-impacted tropical forest regions in the world. Research on the patterns and drivers of vegetation change is critical for developing strategies to sustain ecosystem services in the region and to understand how climate and land use change will affect other tropical forests around the globe. We compared six spectral indices calculated from the 2001–2015 MODIS optical-infrared reflectance data with manually-interpreted measurements of woody vegetation cover from high resolution imagery. The tasseled cap wetness (TCW) index was found to have the strongest association with woody vegetation cover, whereas greenness indices, such as the enhanced vegetation index (EVI), had relatively weak associations with woody cover. Trends in woody vegetation cover measured with the TCW index were analyzed using Mann–Kendall statistics and were contrasted with trends in vegetation greenness measured with EVI. In the drier West Sudanian Savanna and Guinean Forest-Savanna Mosaic ecoregions, EVI trends were primarily positive, and TCW trends were primarily negative, suggesting that woody vegetation cover was decreasing, while herbaceous vegetation cover is increasing. In the wettest tropical forests in the Western Guinean Lowland Forest ecoregion, declining trends in both TCW and EVI were indicative of widespread forest degradation resulting from human activities. Across all ecoregions, declines in woody cover were less prevalent in protected areas where human activities were restricted. Multiple lines of evidence suggested that human land use and resource extraction, rather than climate trends or short-term climatic anomalies, were the predominant drivers of recent vegetation change in the UGF region of West Africa. Full article
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Open AccessArticle Pyroclastic Flow Deposits and InSAR: Analysis of Long-Term Subsidence at Augustine Volcano, Alaska
Remote Sens. 2017, 9(1), 4; doi:10.3390/rs9010004
Received: 23 September 2016 / Revised: 4 December 2016 / Accepted: 14 December 2016 / Published: 24 December 2016
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Abstract
Deformation of pyroclastic flow deposits begins almost immediately after emplacement, and continues thereafter for months or years. This study analyzes the extent, volume, thickness, and variability in pyroclastic flow deposits (PFDs) on Augustine Volcano from measuring their deformation rates with interferometric synthetic aperture
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Deformation of pyroclastic flow deposits begins almost immediately after emplacement, and continues thereafter for months or years. This study analyzes the extent, volume, thickness, and variability in pyroclastic flow deposits (PFDs) on Augustine Volcano from measuring their deformation rates with interferometric synthetic aperture radar (InSAR). To conduct this analysis, we obtained 48 SAR images of Augustine Volcano acquired between 1992 and 2010, spanning its most recent eruption in 2006. The data were processed using d-InSAR time-series analysis to measure the thickness of the Augustine PFDs, as well as their surface deformation behavior. Because much of the 2006 PFDs overlie those from the previous eruption in 1986, geophysical models were derived to decompose deformation contributions from the 1986 deposits underlying the measured 2006 deposits. To accomplish this, we introduce an inversion approach to estimate geophysical parameters for both 1986 and 2006 PFDs. Our analyses estimate the expanded volume of pyroclastic flow material deposited during the 2006 eruption to be 3.3 × 107 m3 ± 0.11 × 107 m3, and that PFDs in the northeastern part of Augustine Island reached a maximum thickness of ~31 m with a mean of ~5 m. Similarly, we estimate the expanded volume of PFDs from the 1986 eruption at 4.6 × 107 m3 ± 0.62 × 107 m3, with a maximum thickness of ~31 m, and a mean of ~7 m. Full article
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Open AccessArticle A Method for Estimating the Aerodynamic Roughness Length with NDVI and BRDF Signatures Using Multi-Temporal Proba-V Data
Remote Sens. 2017, 9(1), 6; doi:10.3390/rs9010006
Received: 25 August 2016 / Revised: 16 December 2016 / Accepted: 19 December 2016 / Published: 24 December 2016
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Abstract
Aerodynamic roughness length is an important parameter for surface fluxes estimates. This paper developed an innovative method for estimation of aerodynamic roughness length (z0m) over farmland with a new vegetation index, the Hot-darkspot Vegetation Index (HDVI). To obtain this new index,
[...] Read more.
Aerodynamic roughness length is an important parameter for surface fluxes estimates. This paper developed an innovative method for estimation of aerodynamic roughness length (z0m) over farmland with a new vegetation index, the Hot-darkspot Vegetation Index (HDVI). To obtain this new index, the normalized-difference hot-darkspot index (NDHD) is introduced using a semi-empirical, kernel-driven bidirectional reflectance model with multi-temporal Proba-V 300-m top-of-canopy (TOC) reflectance products. A linear relationship between HDVI and z0m was found during the crop growth period. Wind profiles data from two field automatic weather station (AWS) were used to calibrate the model: one site is in Guantao County in Hai Basin, in which double-cropping systems and crop rotations with summer maize and winter wheat are implemented; the other is in the middle reach of the Heihe River Basin from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, with the main crop of spring maize. The iterative algorithm based on Monin–Obukhov similarity theory is employed to calculate the field z0m from time series. Results show that the relationship between HDVI and z0m is more pronounced than that between NDVI and z0m for spring maize at Yingke site, with an R2 value that improved from 0.636 to 0.772. At Guantao site, HDVI also exhibits better performance than NDVI, with R2 increasing from 0.630 to 0.793 for summer maize and from 0.764 to 0.790 for winter wheat. HDVI can capture the impacts of crop residue on z0m, whereas NDVI cannot. Full article
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Open AccessArticle Can We Go Beyond Burned Area in the Assessment of Global Remote Sensing Products with Fire Patch Metrics?
Remote Sens. 2017, 9(1), 7; doi:10.3390/rs9010007
Received: 19 August 2016 / Revised: 14 December 2016 / Accepted: 21 December 2016 / Published: 25 December 2016
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Abstract
Global burned area (BA) datasets from satellite Earth observations provide information for carbon emission and for Dynamic Global Vegetation Model (DGVM) benchmarking. Fire patch identification from pixel-level information recently emerged as an additional way of providing informative features about fire regimes through the
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Global burned area (BA) datasets from satellite Earth observations provide information for carbon emission and for Dynamic Global Vegetation Model (DGVM) benchmarking. Fire patch identification from pixel-level information recently emerged as an additional way of providing informative features about fire regimes through the analysis of patch size distribution. We evaluated the ability of global BA products to accurately represent morphological features of fire patches, in the fire-prone Brazilian savannas. We used the pixel-level burned area from LANDSAT images, as well as two global products: MODIS MCD45A1 and the European Space Agency (ESA) fire Climate Change Initiative (FIRE_CCI) product for the 2002–2009 time period. Individual fire patches were compared by linear regressions to test the consistency of global products as a source of burned patch shape information. Despite commission and omission errors respectively reaching 0.74 and 0.81 for ESA FIRE_CCI and 0.64 and 0.62 for MCD45A1 when compared to LANDSAT due to missing small fires, correlations between patch areas showed R2 > 0.6 for all comparisons, with a slope of 0.99 between ESA FIRE_CCI and MCD45A1 but a lower slope (0.6–0.8) when compared to the LANDSAT data. Shape complexity between global products was less correlated (R2 = 0.5) with lower values (R2 = 0.2) between global products and LANDSAT data, due to their coarser resolution. For the morphological features of the ellipse fitted over fire patches, R2 reached 0.6 for the ellipse’s eccentricity and varied from 0.4 to 0.8 for its azimuthal directional angle. We conclude that global BA products underestimate total BA as they miss small fires, but they also underestimate burned patch areas. Patch complexity is the least correlated variable, but ellipse features appear to provide information to be further used for quality product assessment, global pyrogeography or DGVM benchmarking. Full article
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Open AccessArticle Detecting Tree Stems from Volumetric TLS Data in Forest Environments with Rich Understory
Remote Sens. 2017, 9(1), 9; doi:10.3390/rs9010009
Received: 19 August 2016 / Revised: 15 December 2016 / Accepted: 21 December 2016 / Published: 28 December 2016
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Abstract
The present study introduces a method to identify tree stems from terrestrial laser scanning (TLS) data. We focused on forest environments of diverse and layered structure, which were technically characterized by strong occlusion effects with regards to laser scanning. The number and distribution
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The present study introduces a method to identify tree stems from terrestrial laser scanning (TLS) data. We focused on forest environments of diverse and layered structure, which were technically characterized by strong occlusion effects with regards to laser scanning. The number and distribution of tree stems are important information for the management of protective forests against natural hazards, for forest inventory, and for ecological studies. Our approach builds upon a three-dimensional (3D) voxel grid transformation of the original point cloud data, followed by two major steps of processing. Firstly, a series of morphological operations removed leaves and branches and left only potential stem segments. Secondly, the stem segments of each tree were combined by a multipart workflow, which uses shape and neighborhood criteria. At the same time, erroneous fragments and noise were removed from the dataset. As a result, each object in the voxel grid was represented by a single connected component referring to one specific tree stem. Testing the method on nine spatially independent plots provided detection rates of 97% for the number and location of stems from mature trees with a diameter >= 12 cm and 84% for smaller trees with a minimum of 130 cm total tree height. In summary, we obtained a dataset covering the number and locations of the stems from both mature and understory trees, while not aiming at a precise reconstruction of the stem shape. Full article
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Open AccessArticle Measuring Leaf Water Content with Dual-Wavelength Intensity Data from Terrestrial Laser Scanners
Remote Sens. 2017, 9(1), 8; doi:10.3390/rs9010008
Received: 24 October 2016 / Revised: 5 December 2016 / Accepted: 21 December 2016 / Published: 25 December 2016
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Abstract
Decreased leaf moisture content, typically measured as equivalent water thickness (EWT), is an early signal of tree stress caused by drought, disease, or pest insects. We investigated the use of two terrestrial laser scanners (TLSs) employing different wavelengths for improving the understanding how
[...] Read more.
Decreased leaf moisture content, typically measured as equivalent water thickness (EWT), is an early signal of tree stress caused by drought, disease, or pest insects. We investigated the use of two terrestrial laser scanners (TLSs) employing different wavelengths for improving the understanding how EWT can be retrieved in a laboratory setting. Two wavelengths were examined for normalizing the effects of varying leaf structure and geometry on the measured intensity. The relationship between laser intensity features, using red (690 nm) and shortwave infrared (1550 nm) wavelengths, and the EWT of individual leaves or groups of needles were determined with and without intensity corrections. To account for wrinkles and curvatures of the leaves and needles, a model describing the relationship between incidence angle and backscattered intensity was applied. Additionally, a reflectance model describing both diffuse and specular reflectance was employed to remove the fraction of specular reflectance from backscattered intensity. A strong correlation (, RMSE = 0.004 g/cm2) was found between a normalized ratio of the two wavelengths and the measured EWT of samples. The applied intensity correction methods did not significantly improve the results of the study. The backscattered intensity responded to changes in EWT but more investigations are needed to test the suitability of TLSs to retrieve EWT in a forest environment. Full article
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Open AccessArticle Forward Scatter Radar for Air Surveillance: Characterizing the Target-Receiver Transition from Far-Field to Near-Field Regions
Remote Sens. 2017, 9(1), 50; doi:10.3390/rs9010050
Received: 13 July 2016 / Revised: 8 December 2016 / Accepted: 5 January 2017 / Published: 8 January 2017
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Abstract
A generalized electromagnetic model is presented in order to predict the response of forward scatter radar (FSR) systems for air-target surveillance applications in both far-field and near-field conditions. The relevant scattering problem is tackled by developing the Helmholtz–Kirchhoff formula and Babinet’s principle to
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A generalized electromagnetic model is presented in order to predict the response of forward scatter radar (FSR) systems for air-target surveillance applications in both far-field and near-field conditions. The relevant scattering problem is tackled by developing the Helmholtz–Kirchhoff formula and Babinet’s principle to express the scattered and the total fields in typical FSR configurations. To fix the distinctive features of this class of problems, our approach is applied here to metallic targets with canonical rectangular shapes illuminated by a plane wave, but the model can straightforwardly be used to account for more general scenarios. By exploiting suitable approximations, a simple analytical formulation is derived allowing us to efficiently describe the characteristics of the FSR response for a target transitioning with respect to the receiver from far-field to near-field regions. The effects of different target electrical sizes and detection distances on the received signal, as well as the impact of the trajectory of the moving object, are evaluated and discussed. All of the results are shown in terms of quantities normalized to the wavelength and can be generalized to different configurations once the carrier frequency of the FSR system is set. The range of validity of the proposed closed-form approach has been checked by means of numerical analyses, involving comparisons also with a customized implementation of a full-wave commercial CAD tool. The outcomes of this study can pave the way for significant extensions on the applicability of the FSR technique. Full article
(This article belongs to the Special Issue Radar Systems for the Societal Challenges)
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Open AccessArticle Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling
Remote Sens. 2017, 9(1), 1; doi:10.3390/rs9010001
Received: 23 August 2016 / Revised: 8 December 2016 / Accepted: 18 December 2016 / Published: 22 December 2016
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Abstract
Satellite-based PM2.5 concentration estimation is growing as a popular solution to map the PM2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with
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Satellite-based PM2.5 concentration estimation is growing as a popular solution to map the PM2.5 spatial distribution due to the insufficiency of ground-based monitoring stations. However, those applications usually suffer from the simple hypothesis that the influencing factors are linearly correlated with PM2.5 concentrations, though non-linear mechanisms indeed exist in their interactions. Taking the Beijing-Tianjin-Hebei (BTH) region in China as a case, this study developed a generalized additive modeling (GAM) method for satellite-based PM2.5 concentration mapping. In this process, the linear and non-linear relationships between PM2.5 variation and associated contributing factors, such as the aerosol optical depth (AOD), industrial sources, land use type, road network, and meteorological variables, were comprehensively considered. The reliability of the GAM models was validated by comparison with typical linear land use regression (LUR) models. Results show that GAM modeling outperforms LUR modeling at both the annual and seasonal scale, with obvious higher model fitting-based adjusted R2 and lower RMSEs. This is confirmed by the cross-validation-based adjusted R2 with values of GAM-based spring, summer, autumn, winter, and annual models, which are 0.92, 0.78, 0.87, 0.85, and 0.90, respectively, while those of LUR models are 0.87, 0.71, 0.84, 0.84, and 0.85, respectively. Different to the LUR-based hypothesis of the “straight line” relations, the “smoothed curves” from GAM-based apportionment analysis reveals that factors contributing to PM2.5 variation are unstable with the alternate linear and non-linear relations. The GAM model-based PM2.5 concentration surfaces clearly demonstrate their superiority in disclosing the heterogeneous PM2.5 concentrations to the discrete observations. It can be concluded that satellite-based PM2.5 concentration mapping could be greatly improved by GAM modeling given its simultaneous considerations of the linear and non-linear influencing mechanisms of PM2.5. Full article
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Open AccessFeature PaperArticle First Results of a Tandem Terrestrial-Unmanned Aerial mapKITE System with Kinematic Ground Control Points for Corridor Mapping
Remote Sens. 2017, 9(1), 60; doi:10.3390/rs9010060
Received: 30 November 2016 / Revised: 1 January 2017 / Accepted: 4 January 2017 / Published: 11 January 2017
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Abstract
In this article, we report about the first results of the mapKITE system, a tandem terrestrial-aerial concept for geodata acquisition and processing, obtained in corridor mapping missions. The system combines an Unmanned Aerial System (UAS) and a Terrestrial Mobile Mapping System (TMMS) operated
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In this article, we report about the first results of the mapKITE system, a tandem terrestrial-aerial concept for geodata acquisition and processing, obtained in corridor mapping missions. The system combines an Unmanned Aerial System (UAS) and a Terrestrial Mobile Mapping System (TMMS) operated in a singular way: real-time waypoints are computed from the TMMS platform and sent to the UAS in a follow-me scheme. This approach leads to a simultaneous acquisition of aerial-plus-ground geodata and, moreover, opens the door to an advanced post-processing approach for sensor orientation. The current contribution focuses on analysing the impact of the new, dynamic Kinematic Ground Control Points (KGCPs), which arise inherently from the mapKITE paradigm, as an alternative to conventional, costly Ground Control Points (GCPs). In the frame of a mapKITE campaign carried out in June 2016, we present results entailing sensor orientation and calibration accuracy assessment through ground check points, and precision and correlation analysis of self-calibration parameters’ estimation. Conclusions indicate that the mapKITE concept eliminates the need for GCPs when using only KGCPs plus a couple of GCPs at each corridor end, achieving check point horizontal accuracy of μ E , N 1.7 px (3.4 cm) and μ h 4.3 px (8.6 cm). Since obtained from a simplified version of the system, these preliminary results are encouraging from a future perspective. Full article
(This article belongs to the Special Issue Recent Trends in UAV Remote Sensing)
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Open AccessArticle A Fully Automatic Instantaneous Fire Hotspot Detection Processor Based on AVHRR Imagery—A TIMELINE Thematic Processor
Remote Sens. 2017, 9(1), 30; doi:10.3390/rs9010030
Received: 26 September 2016 / Revised: 19 December 2016 / Accepted: 28 December 2016 / Published: 2 January 2017
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Abstract
The German Aerospace Center’s (DLR) TIMELINE project aims to develop an operational processing and data management environment to process 30 years of National Oceanic and Atmospheric Administration (NOAA)—Advanced Very High Resolution Radiometer (AVHRR) raw data into L1b, L2 and L3 products. This article
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The German Aerospace Center’s (DLR) TIMELINE project aims to develop an operational processing and data management environment to process 30 years of National Oceanic and Atmospheric Administration (NOAA)—Advanced Very High Resolution Radiometer (AVHRR) raw data into L1b, L2 and L3 products. This article presents the current status of the fully automated L2 active fire hotspot detection processor, which is based on single-temporal datasets in orbit geometry. Three different probability levels of fire detection are provided. The results of the hotspot processor were tested with simulated fire data. Moreover, the processing results of real AVHRR imagery were validated with five different datasets: MODIS hotspots, visually confirmed MODIS hotspots, fire-news data from the European Forest Fire Information System (EFFIS), burnt area mapping of the Copernicus Emergency Management Service (EMS) and data of the Piedmont fire database. Full article
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Open AccessArticle Evaluation of a Phenology-Dependent Response Method for Estimating Leaf Area Index of Rice Across Climate Gradients
Remote Sens. 2017, 9(1), 20; doi:10.3390/rs9010020
Received: 9 August 2016 / Revised: 30 November 2016 / Accepted: 21 December 2016 / Published: 29 December 2016
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Abstract
Accurate estimate of the seasonal leaf area index (LAI) in croplands is required for understanding not only intra- and inter-annual crop development, but also crop management. Lack of consideration in different growth phases in the relationship between LAI and vegetation indices (VI) often
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Accurate estimate of the seasonal leaf area index (LAI) in croplands is required for understanding not only intra- and inter-annual crop development, but also crop management. Lack of consideration in different growth phases in the relationship between LAI and vegetation indices (VI) often results in unsatisfactory estimation in the seasonal course of LAI. In this study, we partitioned the growing season into two phases separated by maximum VI ( VI max ) and applied the general regression model to the data gained from two phases. As an alternative method to capture the influence of seasonal phenological development on the LAI-VI relationship, we developed a consistent development curve method and compared its performance with the general regression approaches. We used the Normalized Difference VI (NDVI) and the Enhanced VI (EVI) from the rice paddy sites in Asia (South Korea and Japan) and Europe (Spain) to examine its applicability across different climate conditions and management cycles. When the general regression method was used, separating the season into two phases resulted in no better estimation than the estimation obtained with the entire season observation due to an abrupt change in seasonal LAI occurring during the transition between the before and after VI max . The consistent development curve method reproduced the seasonal patterns of LAI from both NDVI and EVI across all sites better than the general regression method. Despite less than satisfactory estimation of a local LAI max , the consistent development curve method demonstrates improvement in estimating the seasonal course of LAI. The method can aid in providing accurate seasonal LAI as an input into ecological process-based models. Full article
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Open AccessArticle Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image
Remote Sens. 2017, 9(1), 10; doi:10.3390/rs9010010
Received: 3 November 2016 / Revised: 16 December 2016 / Accepted: 21 December 2016 / Published: 27 December 2016
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Abstract
Scene classification plays an important role in the intelligent processing of High-Resolution Satellite (HRS) remotely sensed images. In HRS image classification, multiple features, e.g., shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features
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Scene classification plays an important role in the intelligent processing of High-Resolution Satellite (HRS) remotely sensed images. In HRS image classification, multiple features, e.g., shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS images. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structures across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications. Full article
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Open AccessArticle Satellite Attitude Determination and Map Projection Based on Robust Image Matching
Remote Sens. 2017, 9(1), 90; doi:10.3390/rs9010090
Received: 22 August 2016 / Revised: 9 January 2017 / Accepted: 16 January 2017 / Published: 20 January 2017
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Abstract
Small satellites have limited payload and their attitudes are sometimes difficult to determine from the limited onboard sensors alone. Wrong attitudes lead to inaccurate map projections and measurements that require post-processing correction. In this study, we propose an automated and robust scheme that
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Small satellites have limited payload and their attitudes are sometimes difficult to determine from the limited onboard sensors alone. Wrong attitudes lead to inaccurate map projections and measurements that require post-processing correction. In this study, we propose an automated and robust scheme that derives the satellite attitude from its observation images and known satellite position by matching land features from an observed image and from well-registered base-map images. The scheme combines computer vision algorithms (i.e., feature detection, and robust optimization) and geometrical constraints of the satellite observation. Applying the proposed method to UNIFORM-1 observations, which is a 50 kg class small satellite, satellite attitudes were determined with an accuracy of 0.02°, comparable to that of star trackers, if the satellite position is accurately determined. Map-projected images can be generated based on the accurate attitudes. Errors in the satellite position can add systematic errors to derived attitudes. The proposed scheme focuses on determining satellite attitude with feature detection algorithms applying to raw satellite images, unlike image registration studies which register already map-projected images. By delivering accurate attitude determination and map projection, the proposed method can improve the image geometries of small satellites, and thus reveal fine-scale information about the Earth. Full article
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Open AccessArticle Monitoring Annual Urban Changes in a Rapidly Growing Portion of Northwest Arkansas with a 20-Year Landsat Record
Remote Sens. 2017, 9(1), 71; doi:10.3390/rs9010071
Received: 6 November 2016 / Revised: 31 December 2016 / Accepted: 9 January 2017 / Published: 13 January 2017
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Abstract
Northwest Arkansas has undergone a significant urban transformation in the past several decades and is considered to be one of the fastest growing regions in the United States. The urban area expansion and the associated demographic increases bring unprecedented pressure to the environment
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Northwest Arkansas has undergone a significant urban transformation in the past several decades and is considered to be one of the fastest growing regions in the United States. The urban area expansion and the associated demographic increases bring unprecedented pressure to the environment and natural resources. To better understand the consequences of urbanization, accurate and long-term depiction on urban dynamics is critical. Although urban mapping activities using remote sensing have been widely conducted, long-term urban growth mapping at an annual pace is rare and the low accuracy of change detection remains a challenge. In this study, a time series Landsat stack covering the period from 1995 to 2015 was employed to detect the urban dynamics in Northwest Arkansas via a two-stage classification approach. A set of spectral indices that have been proven to be useful in urban area extraction together with the original Landsat spectral bands were used in the maximum likelihood classifier and random forest classifier to distinguish urban from non-urban pixels for each year. A temporal trajectory polishing method, involving temporal filtering and heuristic reasoning, was then applied to the sequence of classified urban maps for further improvement. Based on a set of validation samples selected for five distinct years, the average overall accuracy of the final polished maps was 91%, which improved the preliminary classifications by over 10%. Moreover, results from this study also indicated that the temporal trajectory polishing method was most effective with initial low accuracy classifications. The resulting urban dynamic map is expected to provide unprecedented details about the area, spatial configuration, and growing trends of urban land-cover in Northwest Arkansas. Full article
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Open AccessArticle Atmospheric Corrections and Multi-Conditional Algorithm for Multi-Sensor Remote Sensing of Suspended Particulate Matter in Low-to-High Turbidity Levels Coastal Waters
Remote Sens. 2017, 9(1), 61; doi:10.3390/rs9010061
Received: 20 September 2016 / Revised: 15 December 2016 / Accepted: 3 January 2017 / Published: 12 January 2017
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Abstract
The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal
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The accurate measurement of suspended particulate matter (SPM) concentrations in coastal waters is of crucial importance for ecosystem studies, sediment transport monitoring, and assessment of anthropogenic impacts in the coastal ocean. Ocean color remote sensing is an efficient tool to monitor SPM spatio-temporal variability in coastal waters. However, near-shore satellite images are complex to correct for atmospheric effects due to the proximity of land and to the high level of reflectance caused by high SPM concentrations in the visible and near-infrared spectral regions. The water reflectance signal (ρw) tends to saturate at short visible wavelengths when the SPM concentration increases. Using a comprehensive dataset of high-resolution satellite imagery and in situ SPM and water reflectance data, this study presents (i) an assessment of existing atmospheric correction (AC) algorithms developed for turbid coastal waters; and (ii) a switching method that automatically selects the most sensitive SPM vs. ρw relationship, to avoid saturation effects when computing the SPM concentration. The approach is applied to satellite data acquired by three medium-high spatial resolution sensors (Landsat-8/Operational Land Imager, National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite and Aqua/Moderate Resolution Imaging Spectrometer) to map the SPM concentration in some of the most turbid areas of the European coastal ocean, namely the Gironde and Loire estuaries as well as Bourgneuf Bay on the French Atlantic coast. For all three sensors, AC methods based on the use of short-wave infrared (SWIR) spectral bands were tested, and the consistency of the retrieved water reflectance was examined along transects from low- to high-turbidity waters. For OLI data, we also compared a SWIR-based AC (ACOLITE) with a method based on multi-temporal analyses of atmospheric constituents (MACCS). For the selected scenes, the ACOLITE-MACCS difference was lower than 7%. Despite some inaccuracies in ρw retrieval, we demonstrate that the SPM concentration can be reliably estimated using OLI, MODIS and VIIRS, regardless of their differences in spatial and spectral resolutions. Match-ups between the OLI-derived SPM concentration and autonomous field measurements from the Loire and Gironde estuaries’ monitoring networks provided satisfactory results. The multi-sensor approach together with the multi-conditional algorithm presented here can be applied to the latest generation of ocean color sensors (namely Sentinel2/MSI and Sentinel3/OLCI) to study SPM dynamics in the coastal ocean at higher spatial and temporal resolutions. Full article
(This article belongs to the Special Issue Water Optics and Water Colour Remote Sensing)
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Open AccessArticle Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning
Remote Sens. 2017, 9(1), 21; doi:10.3390/rs9010021
Received: 22 October 2016 / Revised: 18 December 2016 / Accepted: 28 December 2016 / Published: 30 December 2016
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Abstract
Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing
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Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods. Full article
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Open AccessArticle Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices
Remote Sens. 2017, 9(1), 81; doi:10.3390/rs9010081
Received: 8 December 2016 / Revised: 30 December 2016 / Accepted: 10 January 2017 / Published: 16 January 2017
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Abstract
Monitoring the reaction of grassland canopies on fertilizer application is of major importance to enable a well-adjusted management supporting a sustainable production of the grass crop. Up to date, grassland managers estimate the nutrient status and growth dynamics of grasslands by costly and
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Monitoring the reaction of grassland canopies on fertilizer application is of major importance to enable a well-adjusted management supporting a sustainable production of the grass crop. Up to date, grassland managers estimate the nutrient status and growth dynamics of grasslands by costly and time-consuming field surveys, which only provide low temporal and spatial data density. Grassland mapping using remotely-sensed Vegetation Indices (VIs) has the potential to contribute to solving these problems. In this study, we explored the potential of VIs for distinguishing five differently-fertilized grassland communities. Therefore, we collected spectral signatures of these communities in a long-term fertilization experiment (since 1941) in Germany throughout the growing seasons 2012–2014. Fifteen VIs were calculated and their seasonal developments investigated. Welch tests revealed that the accuracy of VIs for distinguishing these grassland communities varies throughout the growing season. Thus, the selection of the most promising single VI for grassland mapping was dependent on the date of the spectra acquisition. A random forests classification using all calculated VIs reduced variations in classification accuracy within the growing season and provided a higher overall precision of classification. Thus, we recommend a careful selection of VIs for grassland mapping or the utilization of temporally-stable methods, i.e., including a set of VIs in the random forests algorithm. Full article
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Open AccessArticle NIR-Red Spectra-Based Disaggregation of SMAP Soil Moisture to 250 m Resolution Based on SMAPEx-4/5 in Southeastern Australia
Remote Sens. 2017, 9(1), 51; doi:10.3390/rs9010051
Received: 27 October 2016 / Revised: 12 December 2016 / Accepted: 1 January 2017 / Published: 8 January 2017
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Abstract
To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined
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To meet the demand of regional hydrological and agricultural applications, a new method named near infrared-red (NIR-red) spectra-based disaggregation (NRSD) was proposed to perform a disaggregation of Soil Moisture Active Passive (SMAP) products from 36 km to 250 m resolution. The NRSD combined proposed normalized soil moisture index (NSMI) with SMAP data to obtain 250 m resolution soil moisture mapping. The experiment was conducted in southeastern Australia during SMAP Experiments (SMAPEx) 4/5 and validated with the in situ SMAPEx network. Results showed that NRSD performed a decent downscaling (root-mean-square error (RMSE) = 0.04 m3/m3 and 0.12 m3/m3 during SMAPEx-4 and SMAPEx-5, respectively). Based on the validation, it was found that the proposed NSMI was a new alternative indicator for denoting the heterogeneity of soil moisture at sub-kilometer scales. Attributed to the excellent performance of the NSMI, NRSD has a higher overall accuracy, finer spatial representation within SMAP pixels and wider applicable scope on usability tests for land cover, vegetation density and drought condition than the disaggregation based on physical and theoretical scale change (DISPATCH) has at 250 m resolution. This revealed that the NRSD method is expected to provide soil moisture mapping at 250-resolution for large-scale hydrological and agricultural studies. Full article
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Open AccessArticle Estimating the Biomass of Maize with Hyperspectral and LiDAR Data
Remote Sens. 2017, 9(1), 11; doi:10.3390/rs9010011
Received: 5 September 2016 / Revised: 6 December 2016 / Accepted: 21 December 2016 / Published: 27 December 2016
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Abstract
The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of
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The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H C V of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H C V combined with H m e a n performed better than one LiDAR-derived metric alone in biomass estimation (R2 = 0.835, RMSE = 374.655 g/m2, RMSECV = 393.573 g/m2). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R2 = 0.883, RMSE = 321.092 g/m2, RMSECV = 337.653 g/m2) compared with LiDAR or hyperspectral data alone. Full article
(This article belongs to the Special Issue Fusion of LiDAR Point Clouds and Optical Images)
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Open AccessArticle Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China
Remote Sens. 2017, 9(1), 31; doi:10.3390/rs9010031
Received: 12 December 2016 / Revised: 24 December 2016 / Accepted: 28 December 2016 / Published: 2 January 2017
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Abstract
Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for
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Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data. A reference subpixel inundation percentage (SIP) map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m). The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU), Artificial neural networks (ANN), and Regression tree (RT)). Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R2 = 0.933, RMSE = 8.73%) compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems. Full article
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Open AccessArticle Inside a Cucuteni Settlement: Remote Sensing Techniques for Documenting an Unexplored Eneolithic Site from Northeastern Romania
Remote Sens. 2017, 9(1), 41; doi:10.3390/rs9010041
Received: 8 September 2016 / Revised: 21 November 2016 / Accepted: 28 December 2016 / Published: 6 January 2017
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Abstract
This paper presents recent results of an integrated non-invasive investigation carried out in a previously unexplored settlement from northeastern Romania, belonging to the last great Eneolithic civilisation of Old Europe, the Cucuteni-Trypillia cultural complex. Although there is a long history of research
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This paper presents recent results of an integrated non-invasive investigation carried out in a previously unexplored settlement from northeastern Romania, belonging to the last great Eneolithic civilisation of Old Europe, the Cucuteni-Trypillia cultural complex. Although there is a long history of research concerning this culture, at only a handful of sites has archaeological research completed a comprehensive planimetric image. This makes it impossible to determine a typological evolution of the internal organisation of Cucutenian sites, both diachronically, across the three great phases of the culture (A, A−B and B for the Romanian area), and spatially, from SE Transylvania to the Republic of Moldova, and towards the steppes of the Ukraine. Accordingly, in certain environmental conditions, many essential behavioural aspects of Cucutenian communities are far from understood. Consequently, the generalisation and integration of non-invasive prospecting methods—Light Detection and Ranging (LiDAR), aerial photography, earth resistivity, magnetometry, and their integration through Geographic Information System (GIS)—clearly represents a feasible alternative for deciphering the Cucuteni culture. These complementary investigation methods were applied for this case study, emphasis being put on the conjoint use of datasets from each technique. On the basis of results recently obtained from the Războieni–Dealul Mare/Dealul Boghiu site, innovative characteristics are described concerning intra-site spatial organisation, a typology of the fortification systems, the existence of ritual or delimitation ditches, and the presence of habitations outside fortified areas. Full article
(This article belongs to the Special Issue Archaeological Prospecting and Remote Sensing)
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Open AccessArticle Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation
Remote Sens. 2017, 9(1), 42; doi:10.3390/rs9010042
Received: 5 October 2016 / Revised: 27 December 2016 / Accepted: 31 December 2016 / Published: 6 January 2017
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Abstract
Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and
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Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400–2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R2) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS·m−1 and an RPD of 2.21, and was more suitable for detecting salinization (10–20 dS·m−1) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling. Full article
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Open AccessFeature PaperArticle A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds
Remote Sens. 2017, 9(1), 92; doi:10.3390/rs9010092
Received: 13 November 2016 / Revised: 15 December 2016 / Accepted: 12 January 2017 / Published: 20 January 2017
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Abstract
3D building model reconstruction is of great importance for environmental and urban applications. Airborne light detection and ranging (LiDAR) is a very useful data source for acquiring detailed geometric and topological information of building objects. In this study, we employed a graph-based method
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3D building model reconstruction is of great importance for environmental and urban applications. Airborne light detection and ranging (LiDAR) is a very useful data source for acquiring detailed geometric and topological information of building objects. In this study, we employed a graph-based method based on hierarchical structure analysis of building contours derived from LiDAR data to reconstruct urban building models. The proposed approach first uses a graph theory-based localized contour tree method to represent the topological structure of buildings, then separates the buildings into different parts by analyzing their topological relationships, and finally reconstructs the building model by integrating all the individual models established through the bipartite graph matching process. Our approach provides a more complete topological and geometrical description of building contours than existing approaches. We evaluated the proposed method by applying it to the Lujiazui region in Shanghai, China, a complex and large urban scene with various types of buildings. The results revealed that complex buildings could be reconstructed successfully with a mean modeling error of 0.32 m. Our proposed method offers a promising solution for 3D building model reconstruction from airborne LiDAR point clouds. Full article
(This article belongs to the Special Issue Remote Sensing for 3D Urban Morphology)
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Open AccessArticle Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images
Remote Sens. 2017, 9(1), 22; doi:10.3390/rs9010022
Received: 5 November 2016 / Revised: 19 December 2016 / Accepted: 28 December 2016 / Published: 30 December 2016
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Abstract
Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is important information for predicting the yield of palm oil, monitoring the growing situation of palm trees and maximizing their
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Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is important information for predicting the yield of palm oil, monitoring the growing situation of palm trees and maximizing their productivity, etc. In this paper, we propose a deep learning based framework for oil palm tree detection and counting using high-resolution remote sensing images for Malaysia. Unlike previous palm tree detection studies, the trees in our study area are more crowded and their crowns often overlap. We use a number of manually interpreted samples to train and optimize the convolutional neural network (CNN), and predict labels for all the samples in an image dataset collected through the sliding window technique. Then, we merge the predicted palm coordinates corresponding to the same palm tree into one palm coordinate and obtain the final palm tree detection results. Based on our proposed method, more than 96% of the oil palm trees in our study area can be detected correctly when compared with the manually interpreted ground truth, and this is higher than the accuracies of the other three tree detection methods used in this study. Full article
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Open AccessArticle Estimating Savanna Clumping Index Using Hemispherical Photographs Integrated with High Resolution Remote Sensing Images
Remote Sens. 2017, 9(1), 52; doi:10.3390/rs9010052
Received: 16 September 2016 / Revised: 13 December 2016 / Accepted: 5 January 2017 / Published: 8 January 2017
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Abstract
In contrast to herbaceous canopies and forests, savannas are grassland ecosystems with sparsely distributed individual trees, so the canopy is spatially heterogeneous and open, whereas the woody cover in savannas, e.g., tree cover, adversely affects ecosystem structures and functions. Studies have shown that
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In contrast to herbaceous canopies and forests, savannas are grassland ecosystems with sparsely distributed individual trees, so the canopy is spatially heterogeneous and open, whereas the woody cover in savannas, e.g., tree cover, adversely affects ecosystem structures and functions. Studies have shown that the dynamics of canopy structure are related to available water, climate, and human activities in the form of porosity, leaf area index (LAI), and clumping index (CI). Therefore, it is important to identify the biophysical parameters of savanna ecosystems, and undertake practical actions for savanna conservation and management. The canopy openness presents a challenge for evaluating canopy LAI and other biophysical parameters, as most remotely sensed methods were developed for homogeneous and closed canopies. Clumping index is a key variable that can represent the clumping effect from spatial distribution patterns of components within a canopy. However, it is a difficult task to measure the clumping index of the moderate resolution savanna pixels directly using optical instruments, such as the Tracing Radiation and Architecture of Canopies, LAI-2000 Canopy Analyzer, or digital hemispherical photography. This paper proposed a new method using hemispherical photographs combined with high resolution remote sensing images to estimate the clumping index of savanna canopies. The effects of single tree LAI, crown density, and herbaceous layer on the clumping index of savanna pixels were also evaluated. The proposed method effectively calculated the clumping index of moderate resolution pixels. The clumping indices of two study regions located in Ejina Banner and Weichang were compared with the clumping index product over China’s landmass. Full article
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Open AccessArticle Assessment of Mono- and Split-Window Approaches for Time Series Processing of LST from AVHRR—A TIMELINE Round Robin
Remote Sens. 2017, 9(1), 72; doi:10.3390/rs9010072
Received: 21 October 2016 / Revised: 21 December 2016 / Accepted: 10 January 2017 / Published: 13 January 2017
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Abstract
Processing of land surface temperature from long time series of AVHRR (Advanced Very High Resolution Radiometer) requires stable algorithms, which are well characterized in terms of accuracy, precision and sensitivity. This assessment presents a comparison of four mono-window (Price 1983, Qin et al.,
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Processing of land surface temperature from long time series of AVHRR (Advanced Very High Resolution Radiometer) requires stable algorithms, which are well characterized in terms of accuracy, precision and sensitivity. This assessment presents a comparison of four mono-window (Price 1983, Qin et al., 2001, Jiménez-Muñoz and Sobrino 2003, linear approach) and six split-window algorithms (Price 1984, Becker and Li 1990, Ulivieri et al., 1994, Wan and Dozier 1996, Yu 2008, Jiménez-Muñoz and Sobrino 2008) to estimate LST from top of atmosphere brightness temperatures, emissivity and columnar water vapour. Where possible, new coefficients were estimated matching the spectral response curves of the different AVHRR sensors of the past and present. The consideration of unique spectral response curves is necessary to avoid artificial anomalies and wrong trends when processing time series data. Using simulated data on the base of a large atmospheric profile database covering many different states of the atmosphere, biomes and geographical regions, it was assessed (a) to what accuracy and precision LST can be estimated using before mentioned algorithms and (b) how sensitive the algorithms are to errors in their input variables. It was found, that the split-window algorithms performed almost equally well, differences were found mainly in their sensitivity to input bands, resulting in the Becker and Li 1990 and Price 1984 split-window algorithm to perform best. Amongst the mono-window algorithms, larger deviations occurred in terms of accuracy, precision and sensitivity. The Qin et al., 2001 algorithm was found to be the best performing mono-window algorithm. A short comparison of the application of the Becker and Li 1990 coefficients to AVHRR with the MODIS LST product confirmed the approach to be physically sound. Full article
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Open AccessArticle Mapping Extent Dynamics of Small Lakes Using Downscaling MODIS Surface Reflectance
Remote Sens. 2017, 9(1), 82; doi:10.3390/rs9010082
Received: 11 November 2016 / Revised: 28 December 2016 / Accepted: 11 January 2017 / Published: 17 January 2017
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Abstract
Lake extent is an indicator of water capacity as well as the aquatic ecological and environmental conditions. Due to the small sizes and rapid water dynamics, monitoring the extent of small lakes fluctuating between 2.5 and 30 km2 require observations with both
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Lake extent is an indicator of water capacity as well as the aquatic ecological and environmental conditions. Due to the small sizes and rapid water dynamics, monitoring the extent of small lakes fluctuating between 2.5 and 30 km2 require observations with both high spatial and temporal resolutions. The paper applied an improved surface reflectance (SR) downscaling method (i.e., IMAR (Improved Modified Adaptive Regression model)) to downscale the daily SR acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra platform to a consistent 250-m resolution, and derived monthly water extent of four small lakes in the Tibetan Plateau (Longre Co, Ayonggongma Co, Ayonggama Co, and Ayongwama Co)) from 2000 to 2014. Using Landsat ETM+ acquired on the same date, the downscaled MODIS SR and identified water extent were compared to the original MODIS, observations downscaled using an early SR downscaling method (MAR (Modified Adaptive Regression model)) and Wavelet fusion. The results showed IMAR achieved the highest correlation coefficients (R2) (0.89–0.957 for SR and 0.79–0.933 for water extent). The errors in the derived water extents were significantly decreased comparing to the results of MAR and Wavelet fusion, and lakes morphometry of IMAR is more comparable to Landsat results. The detected lake extents dynamic between 2000 and 2014 were analyzed using the trend and season decomposition model (BFAST), indicating an increasing trend after 2005, and it likely had higher correlations with temperature and precipitation variation in the Tibetan region (R2: 0.598–0.728 and 0.61–0.735, respectively). Full article
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Open AccessArticle A One-Source Approach for Estimating Land Surface Heat Fluxes Using Remotely Sensed Land Surface Temperature
Remote Sens. 2017, 9(1), 43; doi:10.3390/rs9010043
Received: 22 October 2016 / Revised: 14 December 2016 / Accepted: 28 December 2016 / Published: 6 January 2017
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Abstract
The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST) is a key variable in energy balance process and remotely sensed LST
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The partitioning of available energy between sensible heat and latent heat is important for precise water resources planning and management in the context of global climate change. Land surface temperature (LST) is a key variable in energy balance process and remotely sensed LST is widely used for estimating surface heat fluxes at regional scale. However, the inequality between LST and aerodynamic surface temperature (Taero) poses a great challenge for regional heat fluxes estimation in one-source energy balance models. To address this issue, we proposed a One-Source Model for Land (OSML) to estimate regional surface heat fluxes without requirements for empirical extra resistance, roughness parameterization and wind velocity. The proposed OSML employs both conceptual VFC/LST trapezoid model and the electrical analog formula of sensible heat flux (H) to analytically estimate the radiometric-convective resistance (rae) via a quartic equation. To evaluate the performance of OSML, the model was applied to the Soil Moisture-Atmosphere Coupling Experiment (SMACEX) in United States and the Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) in China, using remotely sensed retrievals as auxiliary data sets at regional scale. Validated against tower-based surface fluxes observations, the root mean square deviation (RMSD) of H and latent heat flux (LE) from OSML are 34.5 W/m2 and 46.5 W/m2 at SMACEX site and 50.1 W/m2 and 67.0 W/m2 at MUSOEXE site. The performance of OSML is very comparable to other published studies. In addition, the proposed OSML model demonstrates similar skills of predicting surface heat fluxes in comparison to SEBS (Surface Energy Balance System). Since OSML does not require specification of aerodynamic surface characteristics, roughness parameterization and meteorological conditions with high spatial variation such as wind speed, this proposed method shows high potential for routinely acquisition of latent heat flux estimation over heterogeneous areas. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Open AccessArticle A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements
Remote Sens. 2017, 9(1), 13; doi:10.3390/rs9010013
Received: 14 July 2016 / Revised: 27 November 2016 / Accepted: 21 December 2016 / Published: 27 December 2016
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Abstract
High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in
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High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area. Full article
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Open AccessArticle MODIS Time Series to Detect Anthropogenic Interventions and Degradation Processes in Tropical Pasture
Remote Sens. 2017, 9(1), 73; doi:10.3390/rs9010073
Received: 1 September 2016 / Revised: 28 December 2016 / Accepted: 1 January 2017 / Published: 14 January 2017
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Abstract
The unavoidable diet change in emerging countries, projected for the coming years, will significantly increase the global consumption of animal protein. It is expected that Brazilian livestock production, responsible for close to 15% of global production, be prepared to answer to the increasing
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The unavoidable diet change in emerging countries, projected for the coming years, will significantly increase the global consumption of animal protein. It is expected that Brazilian livestock production, responsible for close to 15% of global production, be prepared to answer to the increasing demand of beef. Consequently, the evaluation of pasture quality at regional scale is important to inform public policies towards a rational land use strategy directed to improve livestock productivity in the country. Our hypothesis is that MODIS images can be used to evaluate the processes of degradation, restoration and renovation of tropical pastures. To test this hypothesis, two field campaigns were performed covering a route of approximately 40,000 km through nine Brazilian states. To characterize the sampled pastures, biophysical parameters were measured and observations about the pastures, the adopted management and the landscape were collected. Each sampled pasture was evaluated using a time series of MODIS EVI2 images from 2000–2012, according to a new protocol based on seven phenological metrics, 14 Boolean criteria and two numerical criteria. The theoretical basis of this protocol was derived from interviews with producers and livestock experts during a third field campaign. The analysis of the MODIS EVI2 time series provided valuable historical information on the type of intervention and on the biological degradation process of the sampled pastures. Of the 782 pastures sampled, 26.6% experienced some type of intervention, 19.1% were under biological degradation, and 54.3% presented neither intervention nor trend of biomass decrease during the period analyzed. Full article
(This article belongs to the Special Issue Remote Sensing of Land Degradation and Drivers of Change)
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Open AccessArticle Decomposing DInSAR Time-Series into 3-D in Combination with GPS in the Case of Low Strain Rates: An Application to the Hyblean Plateau, Sicily, Italy
Remote Sens. 2017, 9(1), 33; doi:10.3390/rs9010033
Received: 23 August 2016 / Revised: 24 November 2016 / Accepted: 22 December 2016 / Published: 4 January 2017
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Abstract
Differential Interferometric SAR (DInSAR) time-series techniques can be used to derive surface displacement rates with accuracies of 1 mm/year, by measuring the one-dimensional distance change between a satellite and the surface over time. However, the slanted direction of the measurements complicates interpretation of
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Differential Interferometric SAR (DInSAR) time-series techniques can be used to derive surface displacement rates with accuracies of 1 mm/year, by measuring the one-dimensional distance change between a satellite and the surface over time. However, the slanted direction of the measurements complicates interpretation of the signal, especially in regions that are subject to multiple deformation processes. The Simultaneous and Integrated Strain Tensor Estimation from Geodetic and Satellite Deformation Measurements (SISTEM) algorithm enables decomposition into a three-dimensional velocity field through joint inversion with GNSS measurements, but has never been applied to interseismic deformation where strain rates are low. Here, we apply SISTEM for the first time to detect tectonic deformation on the Hyblean Foreland Plateau in South-East Sicily. In order to increase the signal-to-noise ratio of the DInSAR data beforehand, we reduce atmospheric InSAR noise using a weather model and combine it with a multi-directional spatial filtering technique. The resultant three-dimensional velocity field allows identification of anthropogenic, as well as tectonic deformation, with sub-centimeter accuracies in areas of sufficient GPS coverage. Our enhanced method allows for a more detailed view of ongoing deformation processes as compared to the single use of either GNSS or DInSAR only and thus is suited to improve assessments of regional seismic hazard. Full article
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Open AccessArticle Multi-Staged NDVI Dependent Snow-Free Land-Surface Shortwave Albedo Narrowband-to-Broadband (NTB) Coefficients and Their Sensitivity Analysis
Remote Sens. 2017, 9(1), 93; doi:10.3390/rs9010093
Received: 29 October 2016 / Revised: 31 December 2016 / Accepted: 11 January 2017 / Published: 20 January 2017
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Abstract
Narrowband-to-broadband conversion is a critical procedure for mapping land-surface broadband albedo using multi-spectral narrowband remote-sensing observations. Due to the significant difference in optical characteristics between soil and vegetation, NTB conversion is influenced by the variation in vegetation coverage on different surface types. To
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Narrowband-to-broadband conversion is a critical procedure for mapping land-surface broadband albedo using multi-spectral narrowband remote-sensing observations. Due to the significant difference in optical characteristics between soil and vegetation, NTB conversion is influenced by the variation in vegetation coverage on different surface types. To reduce this influence, this paper applies an approach that couples NTB coefficient with the NDVI. Multi-staged NDVI dependent NTB coefficient look-up tables (LUT) for Moderate Resolution Imaging Spectroradiometer (MODIS), Polarization and Directionality of Earth’s Reflectance (POLDER) and Advanced Very High Resolution Radiometer (AVHRR) were calculated using 6000 spectra samples collected from two typical spectral databases. Sensitivity analysis shows that NTB conversion is affected more by the NDVI for sensors with fewer band numbers, such as POLDER and AVHRR. Analysis of the validation results based on simulations, in situ measurements and global albedo products indicates that by using the multi-staged NDVI dependent NTB method, the conversion accuracies of these two sensors could be improved by 2%–13% on different NDVI classes compared with the general method. This improvement could be as high as 15%, on average, and 35% on dense vegetative surface compared with the global broadband albedo product of POLDER. This paper shows that it is necessary to consider surface reflectance characteristics associated with the NDVI on albedo-NTB conversion for remote sensors with fewer than five bands. Full article
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Open AccessArticle Evaluation of Methods for Aerodynamic Roughness Length Retrieval from Very High-Resolution Imaging LIDAR Observations over the Heihe Basin in China
Remote Sens. 2017, 9(1), 63; doi:10.3390/rs9010063
Received: 30 June 2016 / Revised: 21 December 2016 / Accepted: 31 December 2016 / Published: 12 January 2017
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Abstract
The parameterization of heat transfer based on remote sensing data, and the Surface Energy Balance System (SEBS) scheme to retrieve turbulent heat fluxes, already proved to be very appropriate for estimating evapotranspiration (ET) over homogeneous land surfaces. However, the use of such a
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The parameterization of heat transfer based on remote sensing data, and the Surface Energy Balance System (SEBS) scheme to retrieve turbulent heat fluxes, already proved to be very appropriate for estimating evapotranspiration (ET) over homogeneous land surfaces. However, the use of such a method over heterogeneous landscapes (e.g., semi-arid regions or agricultural land) becomes more difficult, since the principle of similarity theory is compromised by the presence of different heat sources at various heights. This study aims to propose and evaluate some models based on vegetation geometry partly developed by Colin and Faivre, to retrieve the surface aerodynamic roughness length for momentum transfer ( z 0 m ), which is a key parameter in the characterization of heat transfer. A new approach proposed by the authors consisted in the use of a Digital Surface Model (DSM) as boundary condition for experiments with a Computational Fluid Dynamics (CFD) model to reproduce 3D wind fields, and to invert them to retrieve a spatialized roughness parameter. Colin and Faivre also applied the geometrical Raupach’s approach for the same purpose. These two methods were evaluated against two empirical ones, widely used in Surface Energy Balance Index (SEBI) based algorithms (Moran; Brutsaert), and also against an alternate geometrical model proposed by Menenti and Ritchie. The investigation was carried out in the Yingke oasis (China) using very-high resolution remote sensing data (VNIR, TIR & LIDAR), for a precise description of the land surface, and a fine evaluation of estimated heat fluxes based on in-situ measurements. A set of five numerical experiments was carried out to evaluate each roughness model. It appears that methods used in experiments 2 (based on Brutsaert) and 4 (based on Colin and Faivre) are the most accurate to estimate the aerodynamic roughness length, according to the estimated heat fluxes. However, the formulation used in experiment 2 allows to minimize errors in both latent and sensible heat flux, and to preserve a good partitioning. An additional evaluation of these two methods based on another k B 1 parameterization could be necessary, given that the latter is not always compatible with the CFD-based retrieval method. Full article
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Open AccessArticle Improving the Downscaling of Diurnal Land Surface Temperatures Using the Annual Cycle Parameters as Disaggregation Kernels
Remote Sens. 2017, 9(1), 23; doi:10.3390/rs9010023
Received: 14 November 2016 / Revised: 22 December 2016 / Accepted: 28 December 2016 / Published: 30 December 2016
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Abstract
The downscaling of geostationary diurnal thermal data can ease the lack of land surface temperature (LST) datasets that combine high spatial and temporal resolution. However, the downscaling of diurnal LST data is more demanding than single scenes. This is because the spatiotemporal interrelationships
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The downscaling of geostationary diurnal thermal data can ease the lack of land surface temperature (LST) datasets that combine high spatial and temporal resolution. However, the downscaling of diurnal LST data is more demanding than single scenes. This is because the spatiotemporal interrelationships of the original LST data have to be preserved and accurately reproduced by the downscaled LST (DLST) data. To that end, LST disaggregation kernels/predictors that provide information about the spatial distribution of LST during different times of a day can prove especially useful. Such LST predictors are the LST Annual Cycle Parameters (ACPs). In this work, multitemporal ACPs are employed for downscaling daytime and nighttime ~4 km geostationary LST from SEVIRI (Spinning Enhanced Visible and Infrared Imager) down to 1 km. The overall goal is to assess if the use of the ACPs can improve the estimation of the diurnal range of DLST (daytime DLST minus nighttime DLST). The evaluation is performed by comparing the DLST diurnal range maps with reference data from MODIS (Moderate Imaging Spectroradiometer) and also with data retrieved from a modified version of the TsHARP (Thermal Sharpening) algorithm. The results suggest that the ACPs increase the downscaling performance, improve the estimation of diurnal DLST range and produce more accurate spatial patterns. Full article
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Open AccessArticle Image Fusion for Spatial Enhancement of Hyperspectral Image via Pixel Group Based Non-Local Sparse Representation
Remote Sens. 2017, 9(1), 53; doi:10.3390/rs9010053
Received: 2 September 2016 / Revised: 28 December 2016 / Accepted: 3 January 2017 / Published: 9 January 2017
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Abstract
Restricted by technical and budget constraints, hyperspectral images (HSIs) are usually obtained with low spatial resolution. In order to improve the spatial resolution of a given hyperspectral image, a new spatial and spectral image fusion approach via pixel group based non-local sparse representation
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Restricted by technical and budget constraints, hyperspectral images (HSIs) are usually obtained with low spatial resolution. In order to improve the spatial resolution of a given hyperspectral image, a new spatial and spectral image fusion approach via pixel group based non-local sparse representation is proposed, which exploits the spectral sparsity and spectral non-local self-similarity of the hyperspectral image. The proposed approach fuses the hyperspectral image with a high-spatial-resolution multispectral image of the same scene to obtain a hyperspectral image with high spatial and spectral resolutions. The input hyperspectral image is used to train the spectral dictionary, while the sparse codes of the desired HSI are estimated by jointly encoding the similar pixels in each pixel group extracted from the high-spatial-resolution multispectral image. To improve the accuracy of the pixel group based non-local sparse representation, the similar pixels in a pixel group are selected by utilizing both the spectral and spatial information. The performance of the proposed approach is tested on two remote sensing image datasets. Experimental results suggest that the proposed method outperforms a number of sparse representation based fusion techniques, and can preserve the spectral information while recovering the spatial details under large magnification factors. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
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Open AccessArticle DInSAR-Based Detection of Land Subsidence and Correlation with Groundwater Depletion in Konya Plain, Turkey
Remote Sens. 2017, 9(1), 83; doi:10.3390/rs9010083
Received: 30 June 2016 / Revised: 5 January 2017 / Accepted: 10 January 2017 / Published: 17 January 2017
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Abstract
In areas where groundwater overexploitation occurs, land subsidence triggered by aquifer compaction is observed, resulting in high socio-economic impacts for the affected communities. In this paper, we focus on the Konya region, one of the leading economic centers in the agricultural and industrial
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In areas where groundwater overexploitation occurs, land subsidence triggered by aquifer compaction is observed, resulting in high socio-economic impacts for the affected communities. In this paper, we focus on the Konya region, one of the leading economic centers in the agricultural and industrial sectors in Turkey. We present a multi-source data approach aimed at investigating the complex and fragile environment of this area which is heavily affected by groundwater drawdown and ground subsidence. In particular, in order to analyze the spatial and temporal pattern of the subsidence process we use the Small BAseline Subset DInSAR technique to process two datasets of ENVISAT SAR images spanning the 2002–2010 period. The produced ground deformation maps and associated time-series allow us to detect a wide land subsidence extending for about 1200 km2 and measure vertical displacements reaching up to 10 cm in the observed time interval. DInSAR results, complemented with climatic, stratigraphic and piezometric data as well as with land-cover changes information, allow us to give more insights on the impact of climate changes and human activities on groundwater resources depletion and land subsidence. Full article
(This article belongs to the Special Issue Earth Observations for Geohazards)
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Open AccessArticle The Effect of Algal Blooms on Carbon Emissions in Western Lake Erie: An Integration of Remote Sensing and Eddy Covariance Measurements
Remote Sens. 2017, 9(1), 44; doi:10.3390/rs9010044
Received: 17 October 2016 / Revised: 13 December 2016 / Accepted: 1 January 2017 / Published: 6 January 2017
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Abstract
Lakes are important components for regulating carbon cycling within landscapes. Most lakes are regarded as CO2 sources to the atmosphere, except for a few eutrophic ones. Algal blooms are common phenomena in many eutrophic lakes and can cause many environmental stresses, yet
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Lakes are important components for regulating carbon cycling within landscapes. Most lakes are regarded as CO2 sources to the atmosphere, except for a few eutrophic ones. Algal blooms are common phenomena in many eutrophic lakes and can cause many environmental stresses, yet their effects on the net exchange of CO2 (FCO2) at large spatial scales have not been adequately addressed. We integrated remote sensing and Eddy Covariance (EC) technologies to investigate the effects that algal blooms have on FCO2 in the western basin of Lake Erie—a large lake infamous for these blooms. Three years of long-term EC data (2012–2014) at two sites were analyzed. We found that at both sites: (1) daily FCO2 significantly correlated with daily temperature, light, and wind speed during the algal bloom periods; (2) monthly FCO2 was negatively correlated with chlorophyll-a concentration; and (3) the year with larger algal blooms was always associated with lower carbon emissions. We concluded that large algal blooms could reduce carbon emissions in the western basin of Lake Erie. However, considering the complexity of processes within large lakes, the weak relationship we found, and the potential uncertainties that remain in our estimations of FCO2 and chlorophyll-a, we argue that additional data and analyses are needed to validate our conclusion and examine the underlying regulatory mechanisms. Full article
(This article belongs to the Special Issue Remote Sensing of Climate Change and Water Resources)
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Open AccessArticle Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space
Remote Sens. 2017, 9(1), 14; doi:10.3390/rs9010014
Received: 17 October 2016 / Revised: 29 November 2016 / Accepted: 22 December 2016 / Published: 27 December 2016
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Abstract
Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user’s requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D
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Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user’s requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D building models rather than from point clouds, resulting in heavy costs and inflexible adaptivity. The scale space is a sound theory for multi-scale representation of an object from a coarser level to a finer level. Therefore, this paper proposes a novel method to reconstruct buildings at different LoDs from airborne Light Detection and Ranging (LiDAR) point clouds based on an improved morphological scale space. The proposed method first extracts building candidate regions following the separation of ground and non-ground points. For each building candidate region, the proposed method generates a scale space by iteratively using the improved morphological reconstruction with the increase of scale, and constructs the corresponding topological relationship graphs (TRGs) across scales. Secondly, the proposed method robustly extracts building points by using features based on the TRG. Finally, the proposed method reconstructs each building at different LoDs according to the TRG. The experiments demonstrate that the proposed method robustly extracts the buildings with details (e.g., door eaves and roof furniture) and illustrate good performance in distinguishing buildings from vegetation or other objects, while automatically reconstructing building LoDs from the finest building points. Full article
(This article belongs to the Special Issue Airborne Laser Scanning)
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Open AccessArticle Modeling and Partitioning of Regional Evapotranspiration Using a Satellite-Driven Water-Carbon Coupling Model
Remote Sens. 2017, 9(1), 54; doi:10.3390/rs9010054
Received: 18 October 2016 / Revised: 1 January 2017 / Accepted: 4 January 2017 / Published: 10 January 2017
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Abstract
The modeling and partitioning of regional evapotranspiration (ET) are key issues in global hydrological and ecological research. We incorporated a stomatal conductance model and a light-use efficiency-based gross primary productivity (GPP) model into the Shuttleworth–Wallace model to develop a simplified carbon-water coupling model,
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The modeling and partitioning of regional evapotranspiration (ET) are key issues in global hydrological and ecological research. We incorporated a stomatal conductance model and a light-use efficiency-based gross primary productivity (GPP) model into the Shuttleworth–Wallace model to develop a simplified carbon-water coupling model, SWH, for estimating ET using meteorological and remote sensing data. To enable regional application of the SWH model, we optimized key parameters with measurements from global eddy covariance (EC) tower sites. In addition, we estimated soil water content with the principle of the bucket system. The model prediction of ET agreed well with the estimates obtained with the EC measurements, with an average R2 of 0.77 and a root mean square error of 0.72 mm·day−1. The model performance was generally better for woody ecosystems than herbaceous ecosystems. Finally, the spatial patterns of ET and relevant model outputs (i.e., GPP, water-use efficiency and the ratio of soil water evaporation to ET) in China with the model simulations were assessed. Full article
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Open AccessArticle Intercomparison of XH2O Data from the GOSAT TANSO-FTS (TIR and SWIR) and Ground-Based FTS Measurements: Impact of the Spatial Variability of XH2O on the Intercomparison
Remote Sens. 2017, 9(1), 64; doi:10.3390/rs9010064
Received: 4 November 2016 / Revised: 28 December 2016 / Accepted: 5 January 2017 / Published: 12 January 2017
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Abstract
Spatial and temporal variability of atmospheric water vapor (H2O) is extremely high, and therefore it is difficult to accurately evaluate the measurement precision of H2O data by a simple comparison between the data derived from two different instruments. We
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Spatial and temporal variability of atmospheric water vapor (H2O) is extremely high, and therefore it is difficult to accurately evaluate the measurement precision of H2O data by a simple comparison between the data derived from two different instruments. We determined the measurement precisions of column-averaged dry-air mole fractions of H2O (XH2O) retrieved independently from spectral radiances in the thermal infrared (TIR) and the short-wavelength infrared (SWIR) regions measured using a Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT), by an intercomparison between the two TANSO-FTS XH2O data products and the ground-based FTS XH2O data. Furthermore, the spatial variability of XH2O was also estimated in the intercomparison process. Mutually coincident XH2O data above land for the period ranging from April 2009 to May 2014 were intercompared with different spatial coincidence criteria. We found that the precisions of the TANSO-FTS TIR and TANSO-FTS SWIR XH2O were 7.3%–7.7% and 3.5%–4.5%, respectively, and that the spatial variability of XH2O was 6.7% within a radius of 50 km and 18.5% within a radius of 200 km. These results demonstrate that, in order to accurately evaluate the measurement precision of XH2O, it is necessary to set more rigorous spatial coincidence criteria or to take into account the spatial variability of XH2O as derived in the present study. Full article
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Open AccessArticle Assessment of Regional Vegetation Response to Climate Anomalies: A Case Study for Australia Using GIMMS NDVI Time Series between 1982 and 2006
Remote Sens. 2017, 9(1), 34; doi:10.3390/rs9010034
Received: 17 August 2016 / Revised: 15 December 2016 / Accepted: 22 December 2016 / Published: 4 January 2017
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Abstract
Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for
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Within the context of climate change, it is of utmost importance to quantify the stability of ecosystems with respect to climate anomalies. It is well acknowledged that ecosystem stability may change over time. As these temporal stability changes may provide a warning for increased vulnerability of the system, this study provides a methodology to quantify and assess these temporal changes in vegetation stability. Within this framework, vegetation stability changes were quantified over Australia from 1982 to 2006 using GIMMS NDVI and climate time series (i.e., SPEI (Standardized Precipitation and Evaporation Index)). Starting from a stability assessment on the complete time series, we aim to assess: (i) the magnitude and direction of stability changes; and (ii) the similarity in these changes for different stability metrics, i.e., the standard deviation of the NDVI anomaly (SD), auto-correlation at lag one of the NDVI anomaly (AC) and the correlation of NDVI anomaly with SPEI (CS). Results show high variability in magnitude and direction for the different stability metrics. Large areas and types of Australian vegetation showed an increase in variability (SD) over time; however, vegetation memory (AC) decreased. The association of NDVI anomalies with drought events (CS) showed a mixed response: the association increased in the western part, while it decreased in the eastern part. This methodology shows the potential for quantifying vegetation responses to major climate shifts and land use change, but results could be enhanced with higher resolution time series data. Full article
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Open AccessArticle Comparison of SEVIRI-Derived Cloud Occurrence Frequency and Cloud-Top Height with A-Train Data
Remote Sens. 2017, 9(1), 24; doi:10.3390/rs9010024
Received: 7 October 2016 / Revised: 13 December 2016 / Accepted: 22 December 2016 / Published: 30 December 2016
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Abstract
To investigate the characteristics of Spinning Enhanced Visible and Infrared Imager (SEVIRI)-derived products from the UK Met Office algorithm, one year of cloud occurrence frequency (COF) and cloud-top height (CTH) data from May 2013 to April 2014 was analysed in comparison with Cloud
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To investigate the characteristics of Spinning Enhanced Visible and Infrared Imager (SEVIRI)-derived products from the UK Met Office algorithm, one year of cloud occurrence frequency (COF) and cloud-top height (CTH) data from May 2013 to April 2014 was analysed in comparison with Cloud Profiling Radar (CPR) and Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) cloud products observed from the A-Train constellation. Because CPR operated in daylight-only data collection mode, daytime products were validated in this study. It is important to note that the different sensor characteristics cause differences in CTH retrievals. The CTH of active instruments, CPR and CALIOP, is derived from the return time of the backscattered radar or LiDAR signal, while the infrared sensor, SEVIRI, measures a radiatively effective CTH. Therefore, some systematic differences in comparison results are expected. However, similarities in spatial distribution and seasonal variability of COFs were noted among SEVIRI, CALIOP, and CPR products, although COF derived by the SEVIRI algorithm showed biases of 14.35% and −3.90% compared with those from CPR and CALIOP measurements, respectively. We found that the SEVIRI algorithm estimated larger COF values than the CPR product, especially over oceans, whereas smaller COF was detected by SEVIRI measurements over land and in the tropics than by CALIOP, where multi-layer clouds and thin cirrus clouds are dominant. CTHs derived from SEVIRI showed better agreement with CPR than with CALIOP. Further comparison with CPR showed that SEVIRI CTH was highly sensitive to the CO2 bias correction used in the Minimum Residual method. Compared with CPR CTHs, SEVIRI has produced stable CTHs since the bias correction update in November 2013, with a correlation coefficient of 0.93, bias of −0.27 km, and standard deviation of 1.61 km. Full article
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Open AccessArticle Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya
Remote Sens. 2017, 9(1), 74; doi:10.3390/rs9010074
Received: 9 September 2016 / Revised: 20 December 2016 / Accepted: 2 January 2017 / Published: 16 January 2017
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Abstract
Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and
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Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis—competing for nutrients and water—has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pléiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the “Woody Weeds” project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pléiades data (OOB accuracy 0.83 and independent reference accuracy 0.87–0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80–0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pléiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures. Full article
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Open AccessFeature PaperArticle Characterization of Active Layer Thickening Rate over the Northern Qinghai-Tibetan Plateau Permafrost Region Using ALOS Interferometric Synthetic Aperture Radar Data, 2007–2009
Remote Sens. 2017, 9(1), 84; doi:10.3390/rs9010084
Received: 2 April 2016 / Revised: 3 January 2017 / Accepted: 10 January 2017 / Published: 17 January 2017
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Abstract
The Qinghai-Tibetan plateau (QTP), also known as the Third Pole and the World Water Tower, is the largest and highest plateau with distinct and competing surface and subsurface processes. It is covered by a large layer of discontinuous and sporadic alpine permafrost which
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The Qinghai-Tibetan plateau (QTP), also known as the Third Pole and the World Water Tower, is the largest and highest plateau with distinct and competing surface and subsurface processes. It is covered by a large layer of discontinuous and sporadic alpine permafrost which has degraded 10% during the past few decades. The average active layer thickness (ALT) increase rate is approximately 7.5 cm·yr−1 from 1995 to 2007, based on soil temperature measurements from 10 borehole sites along Qinghai-Tibetan Highway, and approximately 6.3 cm·yr−1, 2006–2010, using soil temperature profiles for 27 monitoring sites along Qinghai-Tibetan railway. In this study, we estimated the ALT and its AL thickening rate in the northern QTP near the railway using ALOS PALSAR L-band small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) data observed land subsidence and the corresponding ALT modeling. The InSAR estimated ALT and AL thickening rate were validated with ground-based observations from the borehole site WD4 within our study region, indicating excellent agreement. We concluded that we have generated high spatial resolution (30 m) and spatially-varying ALT and AL thickening rates, 2007–2009, over approximately an area of 150 km2 of permafrost-covered region in the northern QTP. Full article
(This article belongs to the Special Issue Remote Sensing in Tibet and Siberia)
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Open AccessArticle Decline of Geladandong Glacier Elevation in Yangtze River’s Source Region: Detection by ICESat and Assessment by Hydroclimatic Data
Remote Sens. 2017, 9(1), 75; doi:10.3390/rs9010075
Received: 3 August 2016 / Revised: 28 November 2016 / Accepted: 10 January 2017 / Published: 14 January 2017
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Abstract
Several studies have indicated that glaciers in the Qinghai-Tibet plateau are thinning, resulting in reduced water supplies to major rivers such as the Yangtze, Yellow, Lancang, Indus, Ganges, Brahmaputra in China, and south Asia. Three rivers in the upstream of Yangtze River originate
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Several studies have indicated that glaciers in the Qinghai-Tibet plateau are thinning, resulting in reduced water supplies to major rivers such as the Yangtze, Yellow, Lancang, Indus, Ganges, Brahmaputra in China, and south Asia. Three rivers in the upstream of Yangtze River originate from glaciers around the Geladandong snow mountain group in central Tibet. Here we used elevation observations from Ice, Cloud, and land Elevation Satellite (ICESat) and reference elevations from a 3-arc-second digital elevation model (DEM) of Shuttle Radar Terrestrial Mission (SRTM), assisted with Landsat-7 images, to detect glacier elevation changes in the western (A), central (B), and eastern (C) regions of Geladandong. Robust fitting was used to determine rates of glacier elevation changes in regions with dense ICESat data, whereas a new method called rate averaging was employed to find rates in regions of low data density. The rate of elevation change was −0.158 ± 0.066 m·a−1 over 2003–2009 in the entire Geladandong and it was −0.176 ± 0.102 m·a−1 over 2003–2008 in Region C (by robust fitting). The rates in Regions A, B, and C were −0.418 ± 0.322 m·a−1 (2000–2009), −0.432 ± 0.020 m·a−1 (2000–2003), and −0.321 ± 0.139 m·a−1 (2000–2008) (by rate averaging). We used in situ hydroclimatic dataset to assess these negative rates: the glacier thinning was caused by temperature rises around Geladandong, based on the temperature records over 1979–2009, 1957–2013, and 1966–2013 at stations Tuotuohe, Wudaoliang, and Anduo. The thinning Geladandong glaciers led to increased discharges recorded at the river gauge stations Tuotuohe and Chumda over 1956–2012. An unabated Geladandong glacier melting will reduce its long-term water supply to the Yangtze River Basin, causing irreversible socioeconomic consequences and seriously degrading the ecological system of the Yangtze River Basin. Full article
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
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Open AccessArticle Spatiotemporal Variability of Land Surface Phenology in China from 2001–2014
Remote Sens. 2017, 9(1), 65; doi:10.3390/rs9010065
Received: 8 September 2016 / Revised: 10 December 2016 / Accepted: 21 December 2016 / Published: 12 January 2017
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Abstract
Land surface phenology is a highly sensitive and simple indicator of vegetation dynamics and climate change. However, few studies on spatiotemporal distribution patterns and trends in land surface phenology across different climate and vegetation types in China have been conducted since 2000, a
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Land surface phenology is a highly sensitive and simple indicator of vegetation dynamics and climate change. However, few studies on spatiotemporal distribution patterns and trends in land surface phenology across different climate and vegetation types in China have been conducted since 2000, a period during which China has experienced remarkably strong El Niño events. In addition, even fewer studies have focused on changes of the end of season (EOS) and length of season (LOS) despite their importance. In this study, we used four methods to reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) dataset and chose the best smoothing result to estimate land surface phenology. Then, the phenophase trends were analyzed via the Mann-Kendall method. We aimed to assess whether trends in land surface phenology have continued since 2000 in China at both national and regional levels. We also sought to determine whether trends in land surface phenology in subtropical or high altitude areas are the same as those observed in high latitude areas and whether those trends are uniform among different vegetation types. The result indicated that the start of season (SOS) was progressively delayed with increasing latitude and altitude. In contrast, EOS exhibited an opposite trend in its spatial distribution, and LOS showed clear spatial patterns over this region that decreased from south to north and from east to west at a national scale. The trend of SOS was advanced at a national level, while the trend in Southern China and the Tibetan Plateau was opposite to that in Northern China. The transaction zone of the SOS within Northern China and Southern China occurred approximately between 31.4°N and 35.2°N. The trend in EOS and LOS were delayed and extended, respectively, at both national and regional levels except that of LOS in the Tibetan Plateau, which was shortened by delayed SOS onset more than by delayed EOS onset. The absolute magnitude of SOS was decreased after 2000 compared with previous studies, and the phenophase trends are species specific. Full article
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Open AccessArticle The Spatiotemporal Pattern of Urban Expansion in China: A Comparison Study of Three Urban Megaregions
Remote Sens. 2017, 9(1), 45; doi:10.3390/rs9010045
Received: 10 November 2016 / Revised: 27 December 2016 / Accepted: 1 January 2017 / Published: 6 January 2017
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Abstract
Urban megaregions have emerged as a new urbanized form. However, previous studies mostly focused on urban expansion at the city scale, particularly for large cities. Understanding urban expansion at the regional scale including cities having different sizes is important for extending current knowledge
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Urban megaregions have emerged as a new urbanized form. However, previous studies mostly focused on urban expansion at the city scale, particularly for large cities. Understanding urban expansion at the regional scale including cities having different sizes is important for extending current knowledge of urban growth and its environmental and ecological impacts. Here, we addressed two questions: (1) How do the extent, rate, and morphological model of urban expansion vary at both the regional and city scales? (2) How do factors, such as city size and expansion rate, influence urban expansion models? We focused on the three largest urban megaregions in China, Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD). We quantified and compared the spatiotemporal pattern of urban expansion during 2000–2010 at both the regional and city scales based on remote sensing data. We used correlation analysis and linear regressions to address our research questions. We found that (1) the three urban megaregions experienced rapid and massive urban growth, but the spatiotemporal pattern varied greatly. Urban expansion was dominated by edge-expansion in the BTH, edge-expansion and infilling in the YRD, and infilling in the PRD. Cities in the same megaregion tended to have similar expansion morphology; (2) geographical location influenced the model of urban expansion the most, followed by city size and by its expansion rate. Small-sized cities were more likely to develop in a leapfrogging model, while cities with relatively rapid expansion tended to grow in an edge-expansion model. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Ecology)
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Open AccessArticle Rebuilding Long Time Series Global Soil Moisture Products Using the Neural Network Adopting the Microwave Vegetation Index
Remote Sens. 2017, 9(1), 35; doi:10.3390/rs9010035
Received: 30 September 2016 / Revised: 16 December 2016 / Accepted: 28 December 2016 / Published: 4 January 2017
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Abstract
This study presents a back propagation neural network (BPNN) method to rebuild a global and long-term soil moisture (SM) series, adopting the microwave vegetation index (MVI). The data used in our study include Soil Moisture and Ocean Salinity (SMOS) Level 3 soil moisture
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This study presents a back propagation neural network (BPNN) method to rebuild a global and long-term soil moisture (SM) series, adopting the microwave vegetation index (MVI). The data used in our study include Soil Moisture and Ocean Salinity (SMOS) Level 3 soil moisture (SMOSL3sm) data, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), and Advanced Microwave Scanning Radiometer 2 (AMSR2) Level 3 brightness temperature (TB) data and L3 SM products. The BPNNs on each grid were trained over July 2010–June 2011, and the entire year of 2013, with SMOSL3sm as a training target, and taking the reflectivities (Rs) of the C/X/Ku/Ka/Q bands, and the MVI from AMSR-E/AMSR2 TB data, as input, in which the MVI is used to correct for vegetation effects. The training accuracy of networks was evaluated by comparing soil moisture products produced using BPNNs (NNsm hereafter) with SMOSL3sm during the BPNN training period, in terms of correlation coefficient (CC), bias (Bias), and the root mean square error (RMSE). Good global results were obtained with CC = 0.67, RMSE = 0.055 m3/m3 and Bias = −0.0005 m3/m3, particularly over Australia, Central USA, and Central Asia. With these trained networks over each pixel, a global and long-term soil moisture time series, i.e., 2003–2015, was built using AMSR-E TB from 2003 to 2011 and AMSR2 TB from 2012 to 2015. Then, NNsm products were evaluated against in situ SM observations from all SCAN (Soil Climate Analysis Network) sites (SCANsm). The results show that NNsm has a good agreement with in situ data, and can capture the temporal dynamics of in situ SM, with CC = 0.52, RMSE = 0.84 m3/m3 and Bias = −0.002 m3/m3. We also evaluate the accuracy of NNsm by comparing with AMSR-E/AMSR2 SM products, with results of a regression method. As a conclusion, this study provides a promising BPNN method adopting MVI to rebuild a long-term SM time series, and this could provide useful insights for the future Water Cycle Observation Mission (WCOM). Full article
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Open AccessArticle Remote Sensing-Based Assessment of the 2005–2011 Bamboo Reproductive Event in the Arakan Mountain Range and Its Relation with Wildfires
Remote Sens. 2017, 9(1), 85; doi:10.3390/rs9010085
Received: 26 July 2016 / Revised: 28 November 2016 / Accepted: 11 January 2017 / Published: 18 January 2017
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Abstract
Pulse ecological events have major impacts on regional and global biogeochemical cycles, potentially inducing a vast set of cascading ecological effects. This study analyzes the widespread reproductive event of bamboo (Melocanna baccifera) that occurred in the Arakan Mountains (Southeast Asia) from
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Pulse ecological events have major impacts on regional and global biogeochemical cycles, potentially inducing a vast set of cascading ecological effects. This study analyzes the widespread reproductive event of bamboo (Melocanna baccifera) that occurred in the Arakan Mountains (Southeast Asia) from 2005 to 2011, and investigates the possible relationship between massive fuel loading due to bamboo synchronous mortality over large areas and wildfire regime. Multiple remote sensing data products are used to map the areal extent of the bamboo-dominated forest. MODIS NDVI time series are then analyzed to detect the spatiotemporal patterns of the reproductive event. Finally, MODIS Active Fire and Burned Area Products are used to investigate the distribution and extension of wildfires before and after the reproductive event. Bamboo dominates about 62,000 km2 of forest in Arakan. Over 65% of the region shows evidence of synchronous bamboo flowering, fruiting, and mortality over large areas, with wave-like spatiotemporal dynamics. A significant change in the regime of wildfires is observed, with total burned area doubling in the bamboo-dominated forest area and reaching almost 16,000 km2. Wildfires also severely affect the remnant patches of the evergreen forest adjacent to the bamboo forest. These results demonstrate a clear interconnection between the 2005–2011 bamboo reproductive event and the wildfires spreading in the region, with potential relevant socio-economic and environmental impacts. Full article
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Open AccessArticle Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery
Remote Sens. 2017, 9(1), 15; doi:10.3390/rs9010015
Received: 5 September 2016 / Revised: 17 December 2016 / Accepted: 21 December 2016 / Published: 29 December 2016
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Abstract
Spectral unmixing and sub-pixel mapping have been used to estimate the proportion and spatial distribution of the different land-cover classes in mixed pixels at a sub-pixel scale. In the past decades, several algorithms were proposed in both categories; however, these two techniques are
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Spectral unmixing and sub-pixel mapping have been used to estimate the proportion and spatial distribution of the different land-cover classes in mixed pixels at a sub-pixel scale. In the past decades, several algorithms were proposed in both categories; however, these two techniques are generally regarded as independent procedures, with most sub-pixel mapping methods using abundance maps generated by spectral unmixing techniques. It should be noted that the utilized abundance map has a strong impact on the performance of the subsequent sub-pixel mapping process. Recently, we built a novel sub-pixel mapping model in combination with the linear spectral mixture model. Therefore, a joint sub-pixel mapping model was established that connects an original (coarser resolution) remotely sensed image with the final sub-pixel result directly. However, this approach focuses on incorporating the spectral information contained in the original image without addressing the spectral endmember variability resulting from variable illumination and environmental conditions. To address this important issue, in this paper we designed a new joint sparse sub-pixel mapping method under the assumption that various representative spectra for each endmember are known a priori and available in a library. In addition, the total variation (TV) regularization was also adopted to exploit the spatial information. The proposed approach was experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method can achieve better results by considering the impact of endmember variability when compared with other sub-pixel mapping methods. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
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Open AccessArticle Wavelet-Based Local Contrast Enhancement for Satellite, Aerial and Close Range Images
Remote Sens. 2017, 9(1), 25; doi:10.3390/rs9010025
Received: 4 November 2016 / Revised: 22 December 2016 / Accepted: 28 December 2016 / Published: 1 January 2017
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Abstract
The methods used for image contrast enhancement in the wavelet domain have been previously documented. The essence of these methods lies in the manipulation of the image during the reconstruction process, by changing the relationship between the components that require transformation. This paper
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The methods used for image contrast enhancement in the wavelet domain have been previously documented. The essence of these methods lies in the manipulation of the image during the reconstruction process, by changing the relationship between the components that require transformation. This paper proposes a new variant based on using undecimated wavelet transform and adapting the Gaussian function for scaling the coefficients of detail wavelet components, so that the role of low coefficients in the reconstructed image is greater. The enhanced image is then created by combining the new components. Applying the Haar wavelet minimises the effects of the relationship disturbance between components, and creates a small buffer around the edge. The proposed method was tested using six images at different scales, collected with handheld photo cameras, and aerial and satellite optical sensors. The results of the tests indicate that the method can achieve comparable, or even better enhancement effects for weak edges, than the well-known unsharp masking and Retinex methods. The proposed method can be applied in order to improve the visual interpretation of remote sensing images taken by various sensors at different scales. Full article
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Open AccessArticle Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series
Remote Sens. 2017, 9(1), 95; doi:10.3390/rs9010095
Received: 16 December 2016 / Revised: 12 January 2017 / Accepted: 16 January 2017 / Published: 22 January 2017
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Abstract
A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic
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A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic production of land cover maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover maps are provided with a confidence map which gives information at the pixel level about the expected quality of the result. Full article
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Open AccessArticle Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives
Remote Sens. 2017, 9(1), 55; doi:10.3390/rs9010055
Received: 20 September 2016 / Revised: 3 January 2017 / Accepted: 4 January 2017 / Published: 11 January 2017
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Abstract
The ability of texture models and red-edge to facilitate the detection of subtle structural vegetation traits could aid in discriminating and mapping grass quantity, a challenge that has been longstanding in the management of grasslands in southern Africa. Subsequently, this work sought to
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The ability of texture models and red-edge to facilitate the detection of subtle structural vegetation traits could aid in discriminating and mapping grass quantity, a challenge that has been longstanding in the management of grasslands in southern Africa. Subsequently, this work sought to explore the robustness of integrating texture metrics and red-edge in predicting the above-ground biomass of grass growing under different levels of mowing and burning in grassland management treatments. Based on the sparse partial least squares regression algorithm, the results of this study showed that red-edge vegetation indices improved above-ground grass biomass from a root mean square error of perdition (RMSEP) of 0.83 kg/m2 to an RMSEP of 0.55 kg/m2. Texture models further improved the accuracy of grass biomass estimation to an RMSEP of 0.35 kg/m2. The combination of texture models and red-edge derivatives (red-edge-derived vegetation indices) resulted in an optimal prediction accuracy of RMSEP 0.2 kg/m2 across all grassland management treatments. These results illustrate the prospect of combining texture metrics with the red-edge in predicting grass biomass across complex grassland management treatments. This offers the detailed spatial information required for grassland policy-making and sustainable grassland management in data-scarce regions such as southern Africa. Full article
(This article belongs to the Special Issue Remote Sensing of Above Ground Biomass)
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Open AccessArticle Evaluation of Landsat-Based METRIC Modeling to Provide High-Spatial Resolution Evapotranspiration Estimates for Amazonian Forests
Remote Sens. 2017, 9(1), 46; doi:10.3390/rs9010046
Received: 4 October 2016 / Revised: 24 December 2016 / Accepted: 28 December 2016 / Published: 6 January 2017
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Abstract
While forest evapotranspiration (ET) dynamics in the Amazon have been studied both as point estimates using flux towers, as well as spatially coarse surfaces using satellite data, higher resolution (e.g., 30 m resolution) ET estimates are necessary to address finer spatial variability associated
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While forest evapotranspiration (ET) dynamics in the Amazon have been studied both as point estimates using flux towers, as well as spatially coarse surfaces using satellite data, higher resolution (e.g., 30 m resolution) ET estimates are necessary to address finer spatial variability associated with forest biophysical characteristics and their changes by natural and human impacts. The objective of this study is to evaluate the potential of the Landsat-based METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model to estimate high-resolution (30 m) forest ET by comparing to flux tower ET (FT ET) data collected over seasonally dry tropical forests in Rondônia, the southwestern region of the Amazon. Analyses were conducted at daily, monthly and seasonal scales for the dry seasons (June–September for Rondônia) of 2000–2002. Overall daily ET comparison between FT ET and METRIC ET across the study site showed r2 = 0.67 with RMSE = 0.81 mm. For seasonal ET comparison, METRIC-derived ET estimates showed an agreement with FT ET measurements during the dry season of r2 >0.70 and %MAE <15%. We also discuss some challenges and potential applications of METRIC for Amazonian forests. Full article
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Open AccessArticle Investigation of Urbanization Effects on Land Surface Phenology in Northeast China during 2001–2015
Remote Sens. 2017, 9(1), 66; doi:10.3390/rs9010066
Received: 15 November 2016 / Revised: 27 December 2016 / Accepted: 8 January 2017 / Published: 12 January 2017
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Abstract
The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS
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The urbanization effects on land surface phenology (LSP) have been investigated by many studies, but few studies have focused on the temporal variations of urbanization effects on LSP. In this study, we used the Moderate-resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI), MODIS Land Surface Temperature (LST) data and China’s Land Use/Cover Datasets (CLUDs) to investigate the temporal variations of urban heat island intensity (UHII) and urbanization effects on LSP in Northeast China during 2001–2015. LST and phenology differences between urban and rural areas represented the urban heat island intensity and urbanization effects on LSP, respectively. A Mann–Kendall nonparametric test and Sen’s slope were used to evaluate the trends of urbanization effects on LSP and urban heat island intensity. The results indicated that the average LSP during 2001–2015 was characterized by high spatial heterogeneity. The start of the growing season (SOS) in old urban areas had become earlier and earlier compared to rural areas, and the differences in SOS between urbanized areas and rural areas changed greatly during 2001–2015 (−0.79 days/year, p < 0.01). Meanwhile, the length of the growing season (LOS) in urban and adjacent areas had become increasingly longer than rural areas, especially in urbanized areas (0.92 days/year, p < 0.01), but the differences in the end of the growing season (EOS) between urban and adjacent areas did not change significantly. Next, the UHII increased in spring and autumn during the whole study period. Moreover, the correlation analysis indicated that the increasing urban heat island intensity in spring contributed greatly to the increases of urbanization effects on SOS, but the increasing urban heat island intensity in autumn did not lead to the increases of urbanization effects on EOS in Northeast China. Full article
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Open AccessArticle A Self-Calibrating Runoff and Streamflow Remote Sensing Model for Ungauged Basins Using Open-Access Earth Observation Data
Remote Sens. 2017, 9(1), 86; doi:10.3390/rs9010086
Received: 17 November 2016 / Revised: 6 January 2017 / Accepted: 10 January 2017 / Published: 18 January 2017
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Abstract
Due to increasing pressures on water resources, there is a need to monitor regional water resource availability in a spatially and temporally explicit manner. However, for many parts of the world, there is insufficient data to quantify stream flow or ground water infiltration
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Due to increasing pressures on water resources, there is a need to monitor regional water resource availability in a spatially and temporally explicit manner. However, for many parts of the world, there is insufficient data to quantify stream flow or ground water infiltration rates. We present the results of a pixel-based water balance formulation to partition rainfall into evapotranspiration, surface water runoff and potential ground water infiltration. The method leverages remote sensing derived estimates of precipitation, evapotranspiration, soil moisture, Leaf Area Index, and a single F coefficient to distinguish between runoff and storage changes. The study produced significant correlations between the remote sensing method and field based measurements of river flow in two Vietnamese river basins. For the Ca basin, we found R2 values ranging from 0.88–0.97 and Nash–Sutcliffe efficiency (NSE) values varying between 0.44–0.88. The R2 for the Red River varied between 0.87–0.93 and NSE values between 0.61 and 0.79. Based on these findings, we conclude that the method allows for a fast and cost-effective way to map water resource availability in basins with no gauges or monitoring infrastructure, without the need for application of sophisticated hydrological models or resource-intensive data. Full article
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Open AccessArticle Stochastic Spatio-Temporal Models for Analysing NDVI Distribution of GIMMS NDVI3g Images
Remote Sens. 2017, 9(1), 76; doi:10.3390/rs9010076
Received: 29 June 2016 / Revised: 23 December 2016 / Accepted: 8 January 2017 / Published: 15 January 2017
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Abstract
The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing
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The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions. Full article
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Open AccessArticle A New Contextual Parameterization of Evaporative Fraction to Reduce the Reliance of the TsVI Triangle Method on the Dry Edge
Remote Sens. 2017, 9(1), 26; doi:10.3390/rs9010026
Received: 29 September 2016 / Revised: 13 December 2016 / Accepted: 17 December 2016 / Published: 4 January 2017
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Abstract
In this study, a new parameterization scheme of evaporative fraction (EF) was developed from the contextual information of remotely sensed radiative surface temperature (Ts) and vegetation index (VI). In the traditional TsVI triangle methods, the Priestley-Taylor
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In this study, a new parameterization scheme of evaporative fraction (EF) was developed from the contextual information of remotely sensed radiative surface temperature ( T s ) and vegetation index (VI). In the traditional T s V I triangle methods, the Priestley-Taylor parameter of each pixel was interpolated for each VI interval; in our proposed new parameterization scheme (NPS), it was performed for each isopiestic line of soil surface moisture. Specifically, of mixed pixels was determined as the weighted-average value of bare soil and full-cover vegetation . The maximum T s of bare soil ( T s m a x ) is the sole parameter needed as the constraint of the dry edge. This has not only bypassed the task involved in the determination of the maximum T s of fully vegetated surface ( T c m a x ), but also made it possible to reduce the reliance of the T s V I triangle methods on the determination of the dry edge. Ground-based measurements taken during 21 days in 2004 were used to validate the EF retrievals. Results show that the accuracy achieved by the NPS is comparable to that achieved by the traditional T s V I triangle methods. Therefore, the simplicity of the proposed new parameterization scheme does not compromise its accuracy in monitoring EF. Full article
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Open AccessArticle Using MODIS Data to Predict Regional Corn Yields
Remote Sens. 2017, 9(1), 16; doi:10.3390/rs9010016
Received: 11 October 2016 / Revised: 14 December 2016 / Accepted: 21 December 2016 / Published: 28 December 2016
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Abstract
A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were
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A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD)) was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield. The yield model YP used LAD from predicted emergence to maturity, and the yield model YF used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model, including the predicted phenology, performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to relatively wide agroclimatic regions with different cultivation methods and for extension to the other crops. However, it needs to be examined further in tropical and sub-tropical regions, which are very different from the two study regions with respect to agroclimatic constraints and agrotechnologies. Full article
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Open AccessArticle Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field
Remote Sens. 2017, 9(1), 96; doi:10.3390/rs9010096
Received: 15 November 2016 / Revised: 9 January 2017 / Accepted: 16 January 2017 / Published: 22 January 2017
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Abstract
This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification. (1) A multilayer region pyramid is constructed
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This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification. (1) A multilayer region pyramid is constructed based on multiscale oversegmentation, and then, CRF is used to model the spatial relationships among those extracted regions within each layer of the region pyramid; the boundary prior knowledge is exploited and integrated into the CRF model as a strengthened constraint to improve classification performance near the boundaries. (2) Multilayer BN is applied to establish the causal connections between adjacent layers of the constructed region pyramid, where the classification probabilities of those sub-regions in the lower layer, conditioned on their parents’ regions in the upper layer, are used as adjacent links. More contextual information is taken into account in this framework, which is a benefit to the performance improvement. Several experiments are conducted on real ESAR and TerraSAR data, and the results show that the proposed method achieves better classification accuracy. Full article
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Open AccessArticle Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community
Remote Sens. 2017, 9(1), 36; doi:10.3390/rs9010036
Received: 6 May 2016 / Revised: 27 December 2016 / Accepted: 28 December 2016 / Published: 11 January 2017
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Abstract
Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90
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Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90 N/90 S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000–2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between 98 % and 100 % . The CCI global map of open water bodies provided the best water class representation (F-score of 89 % ) compared to its constitutive inputs. When focusing on the challenging areas for water bodies’ mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between 74 % and 89 % . The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km 2 ± 0.24 million km 2 . The dataset is freely available through the ESA CCI Land Cover viewer. Full article
(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)
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Open AccessArticle Detection of Water Bodies from AVHRR Data—A TIMELINE Thematic Processor
Remote Sens. 2017, 9(1), 57; doi:10.3390/rs9010057
Received: 18 October 2016 / Revised: 23 December 2016 / Accepted: 28 December 2016 / Published: 10 January 2017
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Abstract
The assessment of water body dynamics is not only in itself a topic of strong demand, but the presence of water bodies is important information when it comes to the derivation of products such as land surface temperature, leaf area index, or snow/ice
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The assessment of water body dynamics is not only in itself a topic of strong demand, but the presence of water bodies is important information when it comes to the derivation of products such as land surface temperature, leaf area index, or snow/ice cover mapping from satellite data. For the TIMELINE project, which aims to derive such products for a long time series of Advanced Very High Resolution Radiometer (AVHRR) data for Europe, precise water masks are therefore not only an important stand-alone product themselves, they are also an essential interstage information layer, which has to be produced automatically after preprocessing of the raw satellite data. The respective orbit segments from AVHRR are usually more than 2000 km wide and several thousand km long, thus leading to fundamentally different observation geometries, including varying sea surface temperatures, wave patterns, and sediment and algae loads. The water detection algorithm has to be able to manage these conditions based on a limited amount of spectral channels and bandwidths. After reviewing and testing already available methods for water body detection, we concluded that they cannot fully overcome the existing challenges and limitations. Therefore an extended approach was implemented, which takes into account the variations of the reflectance properties of water surfaces on a local to regional scale; the dynamic local threshold determination will train itself automatically by extracting a coarse-scale classification threshold, which is refined successively while analyzing subsets of the orbit segment. The threshold is then interpolated by fitting a minimum curvature surface before additional steps also relying on the brightness temperature are included to reduce possible misclassifications. The classification results have been validated using Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and proven an overall accuracy of 93.4%, with the majority of errors being connected to flawed geolocation accuracy of the AVHRR data. The presented approach enables the derivation of long-term water body time series from AVHRR data and is the basis for applied geoscientific studies on large-scale water body dynamics. Full article
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Open AccessArticle Examining Multi-Legend Change Detection in Amazon with Pixel and Region Based Methods
Remote Sens. 2017, 9(1), 77; doi:10.3390/rs9010077
Received: 1 October 2016 / Revised: 5 December 2016 / Accepted: 9 January 2017 / Published: 15 January 2017
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Abstract
Post-classification comparison is one of the most widely used change detection methods. However, it presents several operational problems that are often ignored, such as the occurrence of impossible transitions, difficulties in accuracy assessment and results not accurate enough for the purpose. This work
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Post-classification comparison is one of the most widely used change detection methods. However, it presents several operational problems that are often ignored, such as the occurrence of impossible transitions, difficulties in accuracy assessment and results not accurate enough for the purpose. This work aims to evaluate post-classification comparison change detection results obtained from LANDSAT5/TM data in a region of the Brazilian Amazon, using three legends in different levels of detail and both pixel wise and region based classifiers. A distinctive characteristic of the used approach is that each change mapping is the result of the combination of 100 land cover classifications for each date, obtained using varied training samples. This approach allowed to account for the training samples choice into the methodology, as well as the construction of confidence mappings. We presented and discussed different approaches for evaluating change results, such as the likelihood of land cover transitions occurring within the study area and time gap, the use of rectangular matrices to incorporate the occurrence of impossible or non evaluable changes and classification uncertainty. In general, change mappings obtained from region based classifications showed better results than the ones obtained from pixel based classifications. Globally, the use of region based approaches, in contrast to pixel based ones, led to an increase in accuracy of 15.5% for the change mapping from the most detailed legend, 7.8% for the one with the legend with intermediate level of detail and 3.6% for the less detailed one. In addition, individual transitions between land cover classes were better identified using region based approaches, with the exception of transitions from a non agriculture class to an agricultural one. The proposed quality mappings are useful to help to evaluate the change mappings, mainly in legend levels with higher level of detail and if reference samples are unreliable or unavailable. It was possible to access, in a spatially explicit way, that at least 29.0% of the pixel based change mapping and 21.9% of the region based one from the most detailed legend were erroneous classified, without ground truth information on the evaluated date. These values decreased to 0.5% and 1.4% (respectively the pixel and region based approaches) for results with the legend with the intermediate level of detail and are non existent in the results from the less detailed legend. The more generalized the legend (lower number of classes), the most similar are the accuracy of region and pixel based change mappings. These accuracy values also increase as fewer classes are considered in the legend, since similar classes are assembled during clustering, which reduces the overlap between groups. However, this accuracy is still low for operational purposes in areas with few changes, even considering the very high accuracy of the land cover classifications used to generate the change mappings (land cover classification with Overall Accuracy higher than 0.98 resulted in change mappings with Overall Accuracy around 0.83). Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Advancing NASA’s AirMOSS P-Band Radar Root Zone Soil Moisture Retrieval Algorithm via Incorporation of Richards’ Equation
Remote Sens. 2017, 9(1), 17; doi:10.3390/rs9010017
Received: 30 August 2016 / Revised: 16 November 2016 / Accepted: 21 December 2016 / Published: 28 December 2016
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Abstract
P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the
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P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar observations, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For this reason, an empirical quadratic function (second order polynomial) is currently applied in the AirMOSS inversion algorithm to retrieve the SMP. The three free parameters of the polynomial are retrieved for each AirMOSS pixel using three backscatter observations (i.e., one frequency at three polarizations of Horizontal-Horizontal, Vertical-Vertical and Horizontal-Vertical). In this paper, a more realistic, physically-based SMP model containing three free parameters is derived, based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy. Full article
(This article belongs to the Special Issue Remote Sensing Applied to Soils: From Ground to Space)
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Open AccessArticle Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations
Remote Sens. 2017, 9(1), 47; doi:10.3390/rs9010047
Received: 1 September 2016 / Revised: 20 December 2016 / Accepted: 28 December 2016 / Published: 7 January 2017
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Abstract
Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a
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Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches. Full article
(This article belongs to the Special Issue Digital Forest Resource Monitoring and Uncertainty Analysis)
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Open AccessArticle Gross Primary Production of a Wheat Canopy Relates Stronger to Far Red Than to Red Solar-Induced Chlorophyll Fluorescence
Remote Sens. 2017, 9(1), 97; doi:10.3390/rs9010097
Received: 24 August 2016 / Revised: 5 January 2017 / Accepted: 8 January 2017 / Published: 22 January 2017
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Abstract
Sun-induced chlorophyll fluorescence (SIF) is a radiation flux emitted by chlorophyll molecules in the red (RSIF) and far red region (FRSIF), and is considered as a potential indicator of the functional state of photosynthesis in remote sensing applications. Recently, ground studies and space
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Sun-induced chlorophyll fluorescence (SIF) is a radiation flux emitted by chlorophyll molecules in the red (RSIF) and far red region (FRSIF), and is considered as a potential indicator of the functional state of photosynthesis in remote sensing applications. Recently, ground studies and space observations have demonstrated a strong empirical linear relationship between FRSIF and carbon uptake through photosynthesis (GPP, gross primary production). In this study, we investigated the potential of RSIF and FRSIF to represent the functional status of photosynthesis at canopy level on a wheat crop. RSIF and FRSIF were continuously measured in the O2-B (SIF687) and O2-A bands (SIF760) at a high frequency rate from a nadir view at a height of 21 m, simultaneously with carbon uptake using eddy covariance (EC) techniques. The relative fluorescence yield (Fyield) and the photochemical yield were acquired at leaf level using active fluorescence measurements. SIF was normalized with photosynthetically active radiation (PAR) to derive apparent spectral fluorescence yields (ASFY687, ASFY760). At the diurnal scale, we found limited variations of ASFY687 and ASFY760 during sunny days. We also did not find any link between Fyield and light use efficiency (LUE) derived from EC, which would prevent SIF from indicating LUE changes. The coefficient of determination ( r 2 ) of the linear regression between SIF and GPP is found to be highly variable, depending on the emission wavelength, the time scale of observation, sky conditions, and the phenological stage. Despite its photosystem II (PSII) origin, SIF687 correlates less than SIF760 with GPP in any cases. The strongest SIF–GPP relationship was found for SIF760 during canopy growth. When canopy is in a steady state, SIF687 and SIF760 are almost as effective as PAR in predicting GPP. Our results imply some constraints in the use of simple linear relationships to infer GPP from SIF, as they are expected to be better predictive with far red SIF for canopies with a high dynamic range of green biomass and a low LUE variation range. Full article
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Open AccessArticle A Low-Cost Smartphone Sensor-Based UV Camera for Volcanic SO2 Emission Measurements
Remote Sens. 2017, 9(1), 27; doi:10.3390/rs9010027
Received: 1 November 2016 / Accepted: 28 December 2016 / Published: 1 January 2017
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Abstract
Recently, we reported on the development of low-cost ultraviolet (UV) cameras, based on the modification of sensors designed for the smartphone market. These units are built around modified Raspberry Pi cameras (PiCams; ≈USD 25), and usable system sensitivity was demonstrated in the UVA
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Recently, we reported on the development of low-cost ultraviolet (UV) cameras, based on the modification of sensors designed for the smartphone market. These units are built around modified Raspberry Pi cameras (PiCams; ≈USD 25), and usable system sensitivity was demonstrated in the UVA and UVB spectral regions, of relevance to a number of application areas. Here, we report on the first deployment of PiCam devices in one such field: UV remote sensing of sulphur dioxide emissions from volcanoes; such data provide important insights into magmatic processes and are applied in hazard assessments. In particular, we report on field trials on Mt. Etna, where the utility of these devices in quantifying volcanic sulphur dioxide (SO2) emissions was validated. We furthermore performed side-by-side trials of these units against scientific grade cameras, which are currently used in this application, finding that the two systems gave virtually identical flux time series outputs, and that signal-to-noise characteristics of the PiCam units appeared to be more than adequate for volcanological applications. Given the low cost of these sensors, allowing two-filter SO2 camera systems to be assembled for ≈USD 500, they could be suitable for widespread dissemination in volcanic SO2 monitoring internationally. Full article
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